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   "source": [
    "---\n",
    "# <center>Project Notebook</center>\n",
    "---\n",
    "\n",
    "<right>Author: Hardik B.</right>"
   ]
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   "source": [
    "\n",
    "\n"
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    "# Starbucks Capstone Challenge\n",
    "\n",
    "### Introduction\n",
    "\n",
    "This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. \n",
    "\n",
    "Not all users receive the same offer, and that is the challenge to solve with this data set.\n",
    "\n",
    "Your task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products.\n",
    "\n",
    "Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.\n",
    "\n",
    "You'll be given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. There are also records for when a user completes an offer. \n",
    "\n",
    "Keep in mind as well that someone using the app might make a purchase through the app without having received an offer or seen an offer.\n",
    "\n",
    "### Example\n",
    "\n",
    "To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. The offer is valid for 10 days from receipt. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer.\n",
    "\n",
    "However, there are a few things to watch out for in this data set. Customers do not opt into the offers that they receive; in other words, a user can receive an offer, never actually view the offer, and still complete the offer. For example, a user might receive the \"buy 10 dollars get 2 dollars off offer\", but the user never opens the offer during the 10 day validity period. The customer spends 15 dollars during those ten days. There will be an offer completion record in the data set; however, the customer was not influenced by the offer because the customer never viewed the offer.\n",
    "\n",
    "### Cleaning\n",
    "\n",
    "This makes data cleaning especially important and tricky.\n",
    "\n",
    "You'll also want to take into account that some demographic groups will make purchases even if they don't receive an offer. From a business perspective, if a customer is going to make a 10 dollar purchase without an offer anyway, you wouldn't want to send a buy 10 dollars get 2 dollars off offer. You'll want to try to assess what a certain demographic group will buy when not receiving any offers.\n",
    "\n",
    "### Final Advice\n",
    "\n",
    "Because this is a capstone project, you are free to analyze the data any way you see fit. For example, you could build a machine learning model that predicts how much someone will spend based on demographics and offer type. Or you could build a model that predicts whether or not someone will respond to an offer. Or, you don't need to build a machine learning model at all. You could develop a set of heuristics that determine what offer you should send to each customer (i.e., 75 percent of women customers who were 35 years old responded to offer A vs 40 percent from the same demographic to offer B, so send offer A)."
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    "# Data Sets\n",
    "\n",
    "The data is contained in three files:\n",
    "\n",
    "* portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)\n",
    "* profile.json - demographic data for each customer\n",
    "* transcript.json - records for transactions, offers received, offers viewed, and offers completed\n",
    "\n",
    "Here is the schema and explanation of each variable in the files:\n",
    "\n",
    "**portfolio.json**\n",
    "* id (string) - offer id\n",
    "* offer_type (string) - type of offer ie BOGO, discount, informational\n",
    "* difficulty (int) - minimum required spend to complete an offer\n",
    "* reward (int) - reward given for completing an offer\n",
    "* duration (int) - time for offer to be open, in days\n",
    "* channels (list of strings)\n",
    "\n",
    "**profile.json**\n",
    "* age (int) - age of the customer \n",
    "* became_member_on (int) - date when customer created an app account\n",
    "* gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)\n",
    "* id (str) - customer id\n",
    "* income (float) - customer's income\n",
    "\n",
    "**transcript.json**\n",
    "* event (str) - record description (ie transaction, offer received, offer viewed, etc.)\n",
    "* person (str) - customer id\n",
    "* time (int) - time in hours since start of test. The data begins at time t=0\n",
    "* value - (dict of strings) - either an offer id or transaction amount depending on the record\n",
    "\n",
    "**NOTE: <a id='top'/>:** Running this entire notebook may take some time (in excess of 30 minutes)  "
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   "source": [
    "# Table of contents\n",
    "\n",
    "1. [Problem statement](#ps)\n",
    "2. [Solution Concept](#solution_concept)\n",
    "3. [Machine learning theory](#ml_theroy)\n",
    "4. [Data Exploration & Visualisation](#DE_VS)\n",
    "    1. [Portfolio](#portfolio)\n",
    "    2. [Profile](#profile)\n",
    "    3. [Transcript](#transcript)\n",
    "5. [Data Preprocessing & Advanced Visualisation](#preprocessing)\n",
    "6. [Feature Engineering](#feature)\n",
    "7. [Label Engineering](#lagged)\n",
    "8. [Model training + Evaluation](#profit)\n",
    "9. [Results](#results)\n",
    "10. [Reflection + Future refinement](#results)\n",
    " "
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   "source": [
    "# 1. Problem statement <a id='ps'></a>\n",
    "[Top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project is about the analysis of Starbucks customers who uses its mobile app. The aim is to analyze individual customer behaviors to identify unusual patterns. The finding of such an analysis should help Starbucks business to re-evaluate its rewards program based on specific findings. \n",
    "\n",
    "Ideally, the analysis should profile customers based on their spending behaviors and should connect it with their demographic attributes.\n",
    "\n",
    "In this context, I frame the problem aiming to understand customer behavior through available data points and then providing specific business inputs based on results.\n",
    "\n",
    "We should aim to provide:\n",
    "\n",
    "- Is there any correlation between customer's spending habits and Starbucks reward programs?\n",
    "\n",
    "- Can we identify high paying customers who don't care about the rewards program?\n",
    "\n",
    "- Can we identify customers who haven't made any transactions in the past? Such customers might be pursued individually through a rewards program.\n",
    "- Can we visualise customers interaction with offers and spending activities to gain some insight?\n",
    "\n",
    "If possible, we should combine all the datasets, clean it, and preprocess it so that it applies to the target machine learning model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Solution concept<a id='solution_concept'></a>\n",
    "[Top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are three different datasets, which we should explore one by one and identify any issues like missing information, other inconsistencies, extra/redundant information. We should accomplish this by making good use of visualization as necessary. At the end of this step, we should have a clean dataset ready for further exploration.\n",
    "\n",
    "\n",
    "- Based on the insights gained from data exploration activities, we should be able to identify the modeling concept that makes sense and similar business questions that we want to answer. \n",
    "\n",
    "\n",
    "- We should now combine all the three datasets into one single set and perform some further analysis through advanced visualization charts. Based on this, we should frame a classification problem that we can tackle through some machine learning algorithms.\n",
    "\n",
    "\n",
    "- I have chosen a CatBoost algorithm, a multi-class classification algorithm, via supervised learning. It aims to identify the relations between highly co-related features automatically and comes with built-in visualizations. The primary reason for selecting CatBoost over other standard classification algorithms is that it is straightforward to train and evaluate the model. The final results are usually better with default parameters because CatBoots uses horizontal decision trees internally. Since we are dealing with classification, model accuracy should be our standard evaluation criteria inclding confusion matrix.\n",
    "\n",
    "\n",
    "- In the end, we evaluate the results of the overall analysis, including machine learning model evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Machine learning theory<a id='ml_theroy'></a>\n",
    "[Top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly speaking, machine learning problems can be divided into supervised, semi-supervised, unsupervised, and reinforcement learning.\n",
    "\n",
    "Since we are going to build a supervised learning model to profile customers, let's recap some fundaments of supervised machine learning.\n",
    "Supervised Learning\n",
    "\n",
    "In supervised learning, the dataset is the collection of labeled examples. Each element in the set of examples is called a feature vector. Each attribute of such a feature can be described as the dimension of a feature vector.\n",
    "\n",
    "For instance, in the profile dataset, we have customer id, age, gender, income attributes. Each of these attributes for a specific customer is a dimension of that customer's feature vector. Based on input features, the task is to deduce a label automatically.\n",
    "\n",
    "For instance, the model created using the dataset of profile could take as input a feature vector describing a customer and output a probability that the customer responds to a given offer.\n",
    "\n",
    "Classification is a problem of automatically assigning a label to an unlabeled example. \n",
    "\n",
    "In a classification problem, a label is a member of a finite set of classes. If the size of the set of classes is two (responds or doesn't respond to offer), it is called binary classification problem. Multiclass classification is a classification problem with three or more classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Exploration & Visulisation<a id='DE_VS'></a>\n",
    "[Top](#top)"
   ]
  },
  {
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   "execution_count": 1390,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Enabling notebook extension jupyter-js-widgets/extension...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n"
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   ],
   "source": [
    "!jupyter nbextension enable --py widgetsnbextension"
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       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1040359\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1040359\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\": \"JpP8FXbgAZLkfur7LiK3j9AGBhHNIvF742meBJrjO2ShJDhCG2I1uVvW+0DUtrmc\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\": \"xZlADit0Q04ISQEdKg2k3L4W9AwQBAuDs9nJL9fM/WwzL1tEU9VPNezOFX0nLEAz\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\": \"4BuPRZkdMKSnj3zoxiNrQ86XgNw0rYmBOxe7nshquXwwcauupgBF2DHLVG1WuZlV\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\": \"Dv1SQ87hmDqK6S5OhBf0bCuwAEvL5QYL0PuR/F1SPVhCS/r/abjkbpKDYL2zeM19\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1040359\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1040359' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1040359\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "We will be using bokeh library in addition to matplotlib and seaborn \n",
    "for visualisation of data throghout this notebook.\n",
    "Below is the simple line chart using the bokeh for demostration purpose.\n",
    "\n",
    "You can use the toolbox on the right side of chart to try out different functionalities \n",
    "like zoom-in, zoom-out, reset to analyse specific part of the chart/graph.\n",
    "\n",
    "Bokeh documentatin can be found here: https://docs.bokeh.org/en/latest/index.html\n",
    "'''\n",
    "\n",
    "from bokeh.application import Application\n",
    "from bokeh.application.handlers import FunctionHandler\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.layouts import row, gridplot\n",
    "import scipy.special\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "#default configuration to save bokeh chart upon creation\n",
    "output_file('current_file.html', title='Bokeh Plot', mode='inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"d1a7d613-3a0d-4491-8c8e-572e6a9b883c\" data-root-id=\"1032368\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"5bfbe1c5-f733-44f1-bf9d-3169c3c03ea3\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1032379\"}],\"center\":[{\"id\":\"1032382\"},{\"id\":\"1032386\"},{\"id\":\"1032412\"}],\"left\":[{\"id\":\"1032383\"}],\"renderers\":[{\"id\":\"1032404\"}],\"title\":{\"id\":\"1032369\"},\"toolbar\":{\"id\":\"1032394\"},\"x_range\":{\"id\":\"1032371\"},\"x_scale\":{\"id\":\"1032375\"},\"y_range\":{\"id\":\"1032373\"},\"y_scale\":{\"id\":\"1032377\"}},\"id\":\"1032368\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"axis_label\":\"y\",\"formatter\":{\"id\":\"1032408\"},\"ticker\":{\"id\":\"1032384\"}},\"id\":\"1032383\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1032410\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1032373\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1032392\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1034693\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1032387\",\"type\":\"PanTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1032393\"}},\"id\":\"1032389\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"items\":[{\"id\":\"1032413\"}]},\"id\":\"1032412\",\"type\":\"Legend\"},{\"attributes\":{\"axis\":{\"id\":\"1032379\"},\"ticker\":null},\"id\":\"1032382\",\"type\":\"Grid\"},{\"attributes\":{\"axis\":{\"id\":\"1032383\"},\"dimension\":1,\"ticker\":null},\"id\":\"1032386\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1032377\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1032384\",\"type\":\"BasicTicker\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1032387\"},{\"id\":\"1032388\"},{\"id\":\"1032389\"},{\"id\":\"1032390\"},{\"id\":\"1032391\"},{\"id\":\"1032392\"}]},\"id\":\"1032394\",\"type\":\"Toolbar\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1032393\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"axis_label\":\"x\",\"formatter\":{\"id\":\"1032410\"},\"ticker\":{\"id\":\"1032380\"}},\"id\":\"1032379\",\"type\":\"LinearAxis\"},{\"attributes\":{\"data\":{\"x\":[1,2,3,4,5],\"y\":[6,7,2,4,5]},\"selected\":{\"id\":\"1034692\"},\"selection_policy\":{\"id\":\"1034693\"}},\"id\":\"1032401\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1032388\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1032391\",\"type\":\"ResetTool\"},{\"attributes\":{\"label\":{\"value\":\"Temp.\"},\"renderers\":[{\"id\":\"1032404\"}]},\"id\":\"1032413\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1032375\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"1032401\"}},\"id\":\"1032405\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1032403\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1034692\",\"type\":\"Selection\"},{\"attributes\":{\"text\":\"simple line example\"},\"id\":\"1032369\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1032408\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1032380\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"1032401\"},\"glyph\":{\"id\":\"1032402\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1032403\"},\"selection_glyph\":null,\"view\":{\"id\":\"1032405\"}},\"id\":\"1032404\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1032371\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1032390\",\"type\":\"SaveTool\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1032402\",\"type\":\"Line\"}],\"root_ids\":[\"1032368\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"5bfbe1c5-f733-44f1-bf9d-3169c3c03ea3\",\"root_ids\":[\"1032368\"],\"roots\":{\"1032368\":\"d1a7d613-3a0d-4491-8c8e-572e6a9b883c\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1032368"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#example bokeh line chart\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [6, 7, 2, 4, 5]\n",
    "\n",
    "plot = figure(title= \"simple line\", x_axis_label='x', y_axis_label = 'y')\n",
    "plot.line(x, y, legend_label='Temp.', line_width =2)\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1430,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from jupyterthemes import jtplot\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20.0, 10.0)\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import robust_scale, quantile_transform, scale\n",
    "from matplotlib.figure import Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1395,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose which theme to inherit plotting style from\n",
    "# onedork | grade3 | oceans16 | chesterish | monokai | solarizedl | solarizedd\n",
    "jtplot.style(theme='oceans16')\n",
    "\n",
    "# set \"context\" (paper, notebook, talk, poster)\n",
    "# scale font-size of ticklabels, legend, etc.\n",
    "# remove spines from x and y axes and make grid dashed\n",
    "jtplot.style(context='talk', fscale=1.4, spines=False, gridlines='--')\n",
    "\n",
    "# turn on X- and Y-axis tick marks (default=False)\n",
    "# turn off the axis grid lines (default=True)\n",
    "# and set the default figure size\n",
    "jtplot.style(ticks=True, grid=False, figsize=(6, 4.5))\n",
    "\n",
    "# reset default matplotlib rcParams\n",
    "#jtplot.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1396,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '../data/raw/'\n",
    "\n",
    "# read in the json files\n",
    "profile = pd.read_json(os.path.join(path, 'profile.json'), orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = pd.read_json(path + 'portfolio.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1449,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript = pd.read_json(os.path.join(path, 'transcript.json'), orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A : Portfolio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 6)"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>informational</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reward                      channels  difficulty  duration     offer_type  \\\n",
       "0      10       [email, mobile, social]          10         7           bogo   \n",
       "1      10  [web, email, mobile, social]          10         5           bogo   \n",
       "2       0          [web, email, mobile]           0         4  informational   \n",
       "3       5          [web, email, mobile]           5         7           bogo   \n",
       "4       5                  [web, email]          20        10       discount   \n",
       "\n",
       "                                 id  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "2  3f207df678b143eea3cee63160fa8bed  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7  "
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.200000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.583915</td>\n",
       "      <td>5.831905</td>\n",
       "      <td>2.321398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          reward  difficulty   duration\n",
       "count  10.000000   10.000000  10.000000\n",
       "mean    4.200000    7.700000   6.500000\n",
       "std     3.583915    5.831905   2.321398\n",
       "min     0.000000    0.000000   3.000000\n",
       "25%     2.000000    5.000000   5.000000\n",
       "50%     4.000000    8.500000   7.000000\n",
       "75%     5.000000   10.000000   7.000000\n",
       "max    10.000000   20.000000  10.000000"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['email', 'mobile', 'social'],\n",
       " ['web', 'email', 'mobile', 'social'],\n",
       " ['web', 'email', 'mobile'],\n",
       " ['web', 'email', 'mobile'],\n",
       " ['web', 'email'],\n",
       " ['web', 'email', 'mobile', 'social'],\n",
       " ['web', 'email', 'mobile', 'social'],\n",
       " ['email', 'mobile', 'social'],\n",
       " ['web', 'email', 'mobile', 'social'],\n",
       " ['web', 'email', 'mobile']]"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio['channels'].values.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "Looking at the 'channels' columns, it makes sense to disaggregate the each channel information into separate columns and encode using 0|1 identifiers.\n",
    "\n",
    "Let's write a function for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1397,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generic_one_hot_encoding(col, target_df):\n",
    "    '''\n",
    "    This function\n",
    "    \n",
    "    - Transforms a given 'col' having multiple attributes to separate columns with 0/1 encoding values\n",
    "    \n",
    "    Inputs:\n",
    "     - col : input column \n",
    "       example : channels -> [web, email, mobile, social]\n",
    "    \n",
    "    Return Value:\n",
    "    \n",
    "     - 'target_df' with encoded columns names like [is_web, is_email, is_mobile, is_social]\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    all_enconded_candidates = set()\n",
    "    \n",
    "    for enc_list in target_df[col].values.tolist():\n",
    "        for cnd in enc_list:\n",
    "            all_enconded_candidates.add(cnd)\n",
    "    all_enconded_candidates = list(all_enconded_candidates)\n",
    "    \n",
    "    for cnd in all_enconded_candidates:\n",
    "        target_df['is_' + cnd] = target_df[col].apply(\n",
    "        lambda x: int(cnd in x))\n",
    "    \n",
    "    target_df = target_df.drop(col, axis =1)\n",
    "    \n",
    "    return target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "      <th>is_mobile</th>\n",
       "      <th>is_web</th>\n",
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       "      <th>is_social</th>\n",
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       "      <td>1</td>\n",
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       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>informational</td>\n",
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       "      <th>8</th>\n",
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      ],
      "text/plain": [
       "   reward  difficulty  duration     offer_type  \\\n",
       "0      10          10         7           bogo   \n",
       "1      10          10         5           bogo   \n",
       "2       0           0         4  informational   \n",
       "3       5           5         7           bogo   \n",
       "4       5          20        10       discount   \n",
       "5       3           7         7       discount   \n",
       "6       2          10        10       discount   \n",
       "7       0           0         3  informational   \n",
       "8       5           5         5           bogo   \n",
       "9       2          10         7       discount   \n",
       "\n",
       "                                 id  is_mobile  is_web  is_email  is_social  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd          1       0         1          1  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0          1       1         1          1  \n",
       "2  3f207df678b143eea3cee63160fa8bed          1       1         1          0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9          1       1         1          0  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7          0       1         1          0  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2          1       1         1          1  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4          1       1         1          1  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837          1       0         1          1  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d          1       1         1          1  \n",
       "9  2906b810c7d4411798c6938adc9daaa5          1       1         1          0  "
      ]
     },
     "execution_count": 1398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio = generic_one_hot_encoding('channels', portfolio)\n",
    "portfolio.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- Each indvidual offer is unique even though some of offers belongs to same offer_type. I assume offers belonging to same offer_type more similar to each other than offer belonging to differet offer_type. Hence we should only onsider offer_type as influencing factor in determining the customer behavior\n",
    "- To validate the influence of offers within same offer_type group, a separate A/B test should be conducted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create dummies for offer_type column and encode it and drop the original column as we no longer required it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1399,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will create dummies for the column offer_type and remove the original column\n",
    "\n",
    "portfolio = portfolio.join(pd.get_dummies(portfolio.offer_type))\n",
    "portfolito =portfolio.drop(['offer_type'], axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>is_mobile</th>\n",
       "      <th>is_web</th>\n",
       "      <th>is_email</th>\n",
       "      <th>is_social</th>\n",
       "      <th>bogo</th>\n",
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       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reward  difficulty  duration                                id  is_mobile  \\\n",
       "0      10          10         7  ae264e3637204a6fb9bb56bc8210ddfd          1   \n",
       "1      10          10         5  4d5c57ea9a6940dd891ad53e9dbe8da0          1   \n",
       "2       0           0         4  3f207df678b143eea3cee63160fa8bed          1   \n",
       "3       5           5         7  9b98b8c7a33c4b65b9aebfe6a799e6d9          1   \n",
       "4       5          20        10  0b1e1539f2cc45b7b9fa7c272da2e1d7          0   \n",
       "\n",
       "   is_web  is_email  is_social  bogo  discount  informational  \n",
       "0       0         1          1     1         0              0  \n",
       "1       1         1          1     1         0              0  \n",
       "2       1         1          0     0         0              1  \n",
       "3       1         1          0     1         0              0  \n",
       "4       1         1          0     0         1              0  "
      ]
     },
     "execution_count": 1400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that we should simplify id values by only showing first 6 characters of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1401,
   "metadata": {},
   "outputs": [],
   "source": [
    "#portfolio['id'] = portfolio.id.str[:6]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's re-order the column name to make it more appropriate (i.e. id columns should be first)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1402,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = portfolio[\n",
    "    ['id', 'difficulty', 'reward', \n",
    "     'duration','is_mobile', 'is_web', \n",
    "     'is_social', 'is_email','bogo',\n",
    "     'discount','informational']\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>reward</th>\n",
       "      <th>duration</th>\n",
       "      <th>is_mobile</th>\n",
       "      <th>is_web</th>\n",
       "      <th>is_social</th>\n",
       "      <th>is_email</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  difficulty  reward  duration  is_mobile  \\\n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd          10      10         7          1   \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0          10      10         5          1   \n",
       "2  3f207df678b143eea3cee63160fa8bed           0       0         4          1   \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           5       5         7          1   \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7          20       5        10          0   \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2           7       3         7          1   \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4          10       2        10          1   \n",
       "7  5a8bc65990b245e5a138643cd4eb9837           0       0         3          1   \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d           5       5         5          1   \n",
       "9  2906b810c7d4411798c6938adc9daaa5          10       2         7          1   \n",
       "\n",
       "   is_web  is_social  is_email  bogo  discount  informational  \n",
       "0       0          1         1     1         0              0  \n",
       "1       1          1         1     1         0              0  \n",
       "2       1          0         1     0         0              1  \n",
       "3       1          0         1     1         0              0  \n",
       "4       1          0         1     0         1              0  \n",
       "5       1          1         1     0         1              0  \n",
       "6       1          1         1     0         1              0  \n",
       "7       0          1         1     0         0              1  \n",
       "8       1          1         1     1         0              0  \n",
       "9       1          0         1     0         1              0  "
      ]
     },
     "execution_count": 1403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- Here each offer is always sent via email channel. Therefore this feature is only informative and may not add any value to the predictive power of any machine learning algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move on to Profile dataset, let's briefly consider potential data scaling issue in Portfolio dataframe.\n",
    "We have 'difficulty', 'reward', 'duration' on different numberic scale and unit.\n",
    "\n",
    "We should try to rescale it with mean value around 0 if that is expected by chosen machine learning algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Scaled value')"
      ]
     },
     "execution_count": 1410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axis = plt.subplots(figsize=(20, 5), nrows=1, ncols=2)\n",
    "numeric_columns = ['difficulty','reward','duration']\n",
    "\n",
    "numeric_df = portfolio[numeric_columns]\n",
    "\n",
    "numeric_df.plot.density(ax=axis[0])\n",
    "axis[0].set_title('Unscaled data')\n",
    "axis[0].set_xlabel('Unscaled value')\n",
    "\n",
    "scaled_df = robust_scale(numeric_df)\n",
    "scaled_df = pd.DataFrame(scaled_df, columns=numeric_df.columns, index=numeric_df.index)\n",
    "\n",
    "scaled_df.plot.density(ax=axis[1])\n",
    "axis[1].set_title('Scaled data')\n",
    "axis[1].set_xlabel('Scaled value')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B : Profile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>20170212</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>20170715</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>20180712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>20170509</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>20170804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>20180604</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>20180713</td>\n",
       "      <td>72000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>M</td>\n",
       "      <td>49</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>20170126</td>\n",
       "      <td>73000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>20160307</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>20170722</td>\n",
       "      <td>82000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  age                                id  became_member_on  \\\n",
       "0       None  118  68be06ca386d4c31939f3a4f0e3dd783          20170212   \n",
       "1          F   55  0610b486422d4921ae7d2bf64640c50b          20170715   \n",
       "2       None  118  38fe809add3b4fcf9315a9694bb96ff5          20180712   \n",
       "3          F   75  78afa995795e4d85b5d9ceeca43f5fef          20170509   \n",
       "4       None  118  a03223e636434f42ac4c3df47e8bac43          20170804   \n",
       "...      ...  ...                               ...               ...   \n",
       "16995      F   45  6d5f3a774f3d4714ab0c092238f3a1d7          20180604   \n",
       "16996      M   61  2cb4f97358b841b9a9773a7aa05a9d77          20180713   \n",
       "16997      M   49  01d26f638c274aa0b965d24cefe3183f          20170126   \n",
       "16998      F   83  9dc1421481194dcd9400aec7c9ae6366          20160307   \n",
       "16999      F   62  e4052622e5ba45a8b96b59aba68cf068          20170722   \n",
       "\n",
       "         income  \n",
       "0           NaN  \n",
       "1      112000.0  \n",
       "2           NaN  \n",
       "3      100000.0  \n",
       "4           NaN  \n",
       "...         ...  \n",
       "16995   54000.0  \n",
       "16996   72000.0  \n",
       "16997   73000.0  \n",
       "16998   50000.0  \n",
       "16999   82000.0  \n",
       "\n",
       "[17000 rows x 5 columns]"
      ]
     },
     "execution_count": 1411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation** It seems that age value '118' can not be a real value. It must be used a placeholder for missing value. Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17000.000000</td>\n",
       "      <td>1.700000e+04</td>\n",
       "      <td>14825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.531412</td>\n",
       "      <td>2.016703e+07</td>\n",
       "      <td>65404.991568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.738580</td>\n",
       "      <td>1.167750e+04</td>\n",
       "      <td>21598.299410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.013073e+07</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.016053e+07</td>\n",
       "      <td>49000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.017080e+07</td>\n",
       "      <td>64000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>2.017123e+07</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>118.000000</td>\n",
       "      <td>2.018073e+07</td>\n",
       "      <td>120000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  became_member_on         income\n",
       "count  17000.000000      1.700000e+04   14825.000000\n",
       "mean      62.531412      2.016703e+07   65404.991568\n",
       "std       26.738580      1.167750e+04   21598.299410\n",
       "min       18.000000      2.013073e+07   30000.000000\n",
       "25%       45.000000      2.016053e+07   49000.000000\n",
       "50%       58.000000      2.017080e+07   64000.000000\n",
       "75%       73.000000      2.017123e+07   80000.000000\n",
       "max      118.000000      2.018073e+07  120000.000000"
      ]
     },
     "execution_count": 1412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'F', 'M', None, 'O'}"
      ]
     },
     "execution_count": 1413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(profile.gender.values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>20170212</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>20180712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>20170804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>20170925</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>20171002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>5c686d09ca4d475a8f750f2ba07e0440</td>\n",
       "      <td>20160901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16982</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>d9ca82f550ac4ee58b6299cf1e5c824a</td>\n",
       "      <td>20160415</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16989</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>ca45ee1883624304bac1e4c8a114f045</td>\n",
       "      <td>20180305</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16991</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a9a20fa8b5504360beb4e7c8712f8306</td>\n",
       "      <td>20160116</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16994</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>20151211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2175 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  age                                id  became_member_on  income\n",
       "0       None  118  68be06ca386d4c31939f3a4f0e3dd783          20170212     NaN\n",
       "2       None  118  38fe809add3b4fcf9315a9694bb96ff5          20180712     NaN\n",
       "4       None  118  a03223e636434f42ac4c3df47e8bac43          20170804     NaN\n",
       "6       None  118  8ec6ce2a7e7949b1bf142def7d0e0586          20170925     NaN\n",
       "7       None  118  68617ca6246f4fbc85e91a2a49552598          20171002     NaN\n",
       "...      ...  ...                               ...               ...     ...\n",
       "16980   None  118  5c686d09ca4d475a8f750f2ba07e0440          20160901     NaN\n",
       "16982   None  118  d9ca82f550ac4ee58b6299cf1e5c824a          20160415     NaN\n",
       "16989   None  118  ca45ee1883624304bac1e4c8a114f045          20180305     NaN\n",
       "16991   None  118  a9a20fa8b5504360beb4e7c8712f8306          20160116     NaN\n",
       "16994   None  118  c02b10e8752c4d8e9b73f918558531f7          20151211     NaN\n",
       "\n",
       "[2175 rows x 5 columns]"
      ]
     },
     "execution_count": 1415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[profile['age'] == 118]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation** \n",
    "It is obvious that age value 118 is placeholder for misisng value.\n",
    "The above analysis suggests that 2175 entries are missing for gender, age and income columns togather\n",
    "\n",
    "We should consider removing all rows if we consider any demographic data in order to find insights\n",
    "Currently, let's keep it as-is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    8484\n",
       "F    6129\n",
       "O     212\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 1416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              2175\n",
       "age                    0\n",
       "id                     0\n",
       "became_member_on       0\n",
       "income              2175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        20170212\n",
       "1        20170715\n",
       "2        20180712\n",
       "3        20170509\n",
       "4        20170804\n",
       "           ...   \n",
       "16995    20180604\n",
       "16996    20180713\n",
       "16997    20170126\n",
       "16998    20160307\n",
       "16999    20170722\n",
       "Name: became_member_on, Length: 17000, dtype: int64"
      ]
     },
     "execution_count": 1418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.became_member_on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation** becaome_member_on is time stamped date, let's transform it into more readable format and later extract the number of days since the customer has signed up. This may prove useful later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>2017-02-12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>2018-07-12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age                                id became_member_on    income\n",
       "0   None  118  68be06ca386d4c31939f3a4f0e3dd783       2017-02-12       NaN\n",
       "1      F   55  0610b486422d4921ae7d2bf64640c50b       2017-07-15  112000.0\n",
       "2   None  118  38fe809add3b4fcf9315a9694bb96ff5       2018-07-12       NaN\n",
       "3      F   75  78afa995795e4d85b5d9ceeca43f5fef       2017-05-09  100000.0\n",
       "4   None  118  a03223e636434f42ac4c3df47e8bac43       2017-08-04       NaN"
      ]
     },
     "execution_count": 1419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.became_member_on = pd.to_datetime(profile.became_member_on, format='%Y%m%d', errors='coerce')\n",
    "\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2013-07-29 00:00:00')"
      ]
     },
     "execution_count": 1421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.became_member_on.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation** It seems that earliest sign up is in year 2013. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1422,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['member_since_x_days'] = (\n",
    "pd.to_datetime('today') - profile.became_member_on)\n",
    "profile['member_since_x_days'] = profile['member_since_x_days'].dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "      <th>member_since_x_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>2017-02-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age                                id became_member_on    income  \\\n",
       "0   None  118  68be06ca386d4c31939f3a4f0e3dd783       2017-02-12       NaN   \n",
       "1      F   55  0610b486422d4921ae7d2bf64640c50b       2017-07-15  112000.0   \n",
       "\n",
       "   member_since_x_days  \n",
       "0                 1165  \n",
       "1                 1012  "
      ]
     },
     "execution_count": 1423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's create some visualisations to analyze the demographic data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CsA9mBWVlZSgpKYGqqmqlyeCJiKjhkEgkKCsrQ4sWLWo11RDVH95jiYgah9reY5lgVlBSUlLtfIBERNTwODg4SKe5oYaN91giosblQ++xTDArKM/QHRwcoKampuRoiIioOqWlpbhy5QprLxsR3mOJiBqH2t5jmWBWUN5kR01NjTc/IqJGgE0tGw/eY4mIGpcPvcfy1S8REREREREpBBNMIiIiIiIiUggmmERERERERKQQTDCJiIiIiIhIIeo9wQwNDUVcXJx0OS0tDQKBAI6OjhgzZgzu3r0rXZecnAwvLy84OTlh2rRpePLkSZXH/OOPPzBx4kQ4OTlh0KBBOHHiRJ1fBxEREREREcmqtwTz9OnTiIyMxI8//igtE4vFCAwMhJ+fH1JTU+Hq6orZs2cDAHJychAaGorFixfj5MmT0NfXR1hYWJXHDg0NRadOnXDq1CkEBwdj3rx5+OOPP+rluoiIiIiIiOiNekswMzIyIBaLYWBgIC1LSUmBqakpfHx8oKuri4CAAAiFQty6dQsJCQlwd3eHp6cn9PX1ERQUhF9++QX5+fkyx/3999+Rnp6OkJAQtG3bFt7e3nB0dMTx48fr69KIiIiIiIgI9TgP5qxZswAA48ePl5ZlZWXBzs5Ouqyurg4zMzMIhUJkZWWha9eu0nVGRkbQ1NTEo0ePoKOjIy2/ceMGTExMoK2tLS2zsrLC/fv36/BqiIiIiIiI6G31lmBWRSQSQU9PT6ZMS0sLYrEYIpFIJmksX1dQUFDpGBUTzvLtcnNzazx3bGwsYmNjZco0NDQQHBz8vpdBRNQkjYr6DX++LAEAmBhoQFRYWuVyTeuqWtZt3QLxC5yVcEVUUW5uLoYNG4YNGzagT58+SEtLQ3h4OB48eAB7e3usWLEC5ubmAN6MibBq1So8ffoUvXr1wooVK9C+fXslXwFR/aj4XcjvL6J3U2qCqaOjg6KiIpmywsJC6OrqVrvu7WRSV1cXhYWFVR6jJr6+vvD19ZUpKy0tRWZm5vteBhFRoyBvwli+/OfLEvxZ8GZZp3UL5FezXNO6qpapYfjyyy8hEokA/G9MhODgYAwcOBC7d+/G7Nmz8eOPP0rHRFi3bh26deuG1atXIywsDNu2bVPyFRDVjz/5/UX0XpSaYFpaWuL777+XLhcXF0MoFMLOzg5Xr17FjRs3pOtyc3Px+vVrmJmZyRzD3Nwc9+/fR1FREVq1agUAuH37Nv72t7/VyzUQETUU70og5U0Yy5ep6YqLi0Pr1q3RsWNHALJjIgBAQEAAYmJicOvWLRw7dkw6JgIABAUFYcCAAcjPz6/00peIiEip82B6e3sjOzsbSUlJEIlE0rejhoaGGDZsGI4fP4709HTk5eVh5cqVEAgEUFdXlzmGqakpnJycsHHjRhQUFCAxMRE3btyAh4eHkq6KiKh+jIr6Dd5hafAOS8OkTZelCeSfBSXI///ksuIyEQA8fPgQO3bskBmZ/V1jItjb20vXVRwTgYiI6G1KfUWtra2NzZs3Izw8HPPnz4eLiwuioqIAAGZmZoiIiEBISAjy8vLQv39/hIaGAnhzc/Ty8kJKSgpMTEywatUqLFiwAG5ubujcuTO+/vrrSv03iYgau7drKN+ukSR6F4lEgoULFyI4OBj6+vrSckWMiVARxzkgImq+6v2J5Ntvv5VZdnNzQ1JSUpXbCgQCCASCSuUmJibIzs6WLhsbG1c6LhFRY8eEkhRt3759MDAwwKBBg2TKFTEmQkUc54CIqPniEwoRUQPBhJLq2rlz55CSkoIjR45IyyZNmgRtbW1YW1tLyz5kTAQiIiKACSYRkdIwoaT6tnXrVpllT09PLF++HA4ODvDw8EBSUhL69u2L6OhomTERRowYgfT0dFhYWFQ7JgIRERHABJOIqN4woaSG6kPHRCAiInobn2iIiOoIE0pq6E6cOCH9/UPGRCAiInobn3CIiBSECSURERE1d3ziISKqhfKkkgklEREREaCq7ACIiBqz8qQyv7BU2aEQERERKR1fsRMRvYe3m8ESERER0f8wwSQieg9sBktERERUPT4dERHVgDWWRERERPJjgklEVAPWWBIRERHJj09LREQVsMaSiIiI6MMxwSSiZo1zVxIREREpDp+eiKhZY0JJREREpDh8miKiZoVNYImImraK3/O6rVsgfoGzkiMial6YYBJRs8IaSyKipq3i9zwR1T9VZQdARERERERETQNf3xNRk8YmsURERET1hwkmETVpbBJLREREVH/4tEVETQprLImImr7y73oO4kPU8DDBJKImhTWWRERNHwfyIWq4+PRFRI0aayyJiKg6Oq1bKHTaEkUfj6gp4iiyRNSolb/F/rOgBPmFpcoOh6jBO3z4MDw9PeHk5ISRI0ciPT0dAODv7w9ra2vpT69evaT7JCcnw8vLC05OTpg2bRqePHmirPCJ3lvF+0R5YtiQjkfU1DDBJCIiaibu3r2LiIgILFmyBGfPnsXHH3+MmTNnoqysDA8ePMDx48eRnZ2N7OxsXLhwAQCQk5OD0NBQLF68GCdPnoS+vj7CwsKUfCVERNRQsYksETUqbBJL9OHOnTuHHj16wMPDAwAwevRorFmzBk+ePEFubi6MjY0r7ZOQkAB3d3d4enoCAIKCgjBgwADk5+dDR0enXuMnqg+8zxDVDhNMImpUOIgP0YcbPXo0PvvsM0gkEhQUFGD//v3o1KkTSkpKoKKigrFjxyI7Oxvm5uZYtGgRXFxckJWVha5du0qPYWRkBE1NTTx69IgJJind230iFZEQ8j5DVDv8qyEiImom1NTUoKamhrNnz2LSpEkAgMDAQLx48QIWFhaYP38+rKysEB8fD39/fxw5cgQikQja2toyx9HS0kJBQUG154mNjUVsbKxMmYaGBoKDgxV/UdTsvU9CyEF6iOoeE0wiatDYVIlI8fr06YMrV67g0qVLCAwMhK2tLQ4cOCBdP3HiRMTFxeHixYvQ0dFBUVGRzP6FhYU11l76+vrC19dXpqy0tBSZmZmKvRCiD8ApTojqFgf5IaIGjaPEEinOjh07pImkuro6evbsCWdnZ9y6dQvHjh2T2bakpARaWlqwtLTEjRs3pOW5ubl4/fo1zMzM6jN0IiJqJJhgEhERNRMGBgbYsWMHrl+/jqKiIqSmpiIjIwOlpaVYtmwZMjMzIRaLERMTg+LiYvTs2RPDhg3D8ePHkZ6ejry8PKxcuRICgQDq6urKvhwiImqA2ESWiBoUNoklqjuffvopcnJyMGPGDLx48QJmZmaIjIyEl5cXWrdujaCgIIjFYtjb22Pnzp3Q0NCAmZkZIiIiEBISgry8PPTv3x+hoaHKvhQihaiLQYKImjsmmETUoHD0PqK6o6qqitmzZ2P27NmV1k2ePBmTJ0+ucj+BQACBQFDX4REpBe87RIrFvyIiIiIionrCUWypqWOCSURERERUTziKLTV1TDCJSKnY94WIiIio6WgQo8h+//338Pb2hqOjIwQCAVJSUgAAaWlpEAgEcHR0xJgxY3D37t0q9xeLxZg1axacnZ3h4eGBuLi4+gyfiGqB05AQEVFjVT5IkHdYGrzD0jAq6jdlh0SkdEpPMIVCIcLDw7F27Vqkp6djxowZ0lHsAgMD4efnh9TUVLi6ulY5KAEAREZGori4GMeOHcOaNWsQFRWFq1ev1vOVEBEREVFzU/FFaXmLHKLmTOkJpqqqKtTU1CCRSAAAEokEbdq0wbFjx2BqagofHx/o6uoiICAAQqEQt27dktm/uLgYiYmJCA4ORrt27dCjRw8MGjQI//nPf5RxOURERERERM2W0vtgmpqaYtKkSfD19ZWWrV69GllZWbCzs5OWqaurw8zMDEKhEF26dJGW379/H2VlZbC0tJSWWVlZ4ddff62fCyCi98I+l0RERERNl9ITzPT0dOzduxd79+5F165dcejQISxduhQCgQB6enoy22ppaUEsFsuUiUQi6OjoVNquoKCgxvPGxsYiNjZWpkxDQwPBwcG1uBoiehfON0ZERETUdCn96S4pKQlDhgxBr169AADjx4/H/v37cfbsWXh6espsW1hYCF1dXZkyXV1dFBYWvnO7t/n6+srUmgJAaWkpMjMzP/RSiIiIiIiImjWlJ5itWrVCcXGxTJmamhqmTJki04+yuLgYQqFQptksABgbG6O0tBQ5OTkwNTUFANy+fRu2trZ1HzwRvRObxBIRERE1H0of5MfT0xNHjhzBhQsXUFhYiEOHDiEvLw/Dhg1DdnY2kpKSIBKJsG7dOnTr1g2GhoYy+2tqamLw4MFYv349RCIRzpw5g6NHj0IgECjpioioIk5DQkRE74tTfxA1XkpPMLt3745ly5YhPDwcffr0QWxsLLZv3w5tbW1s3rwZGzduRN++fXHz5k1ERkZK97O2tsaFCxcAAAsXLsSrV6/Qt29fLF26FF999ZW0NpOIiIiIGhdO/UHUeCm9iSwADB06FEOHDq1U7ubmhqSkpCr3yc7Olv6up6eHrVu31ll8REREREQfQtFdRcqPp9u6BeIXONf6eESK1iASTCJqOtjnkoiI6H8UPXp6xeMRNURMMIlIoTgNCREREVHzxac/IiIiIiIl0GndQqblD5u9UlPABJOIiIiISAHeThjl6SrCJq/U1DDBJCIiIiJSEHYVoeZO6dOUEFHjVz5f2aRNl5UdChG9w+HDh+Hp6QknJyeMHDkS6enpAIC0tDQIBAI4OjpizJgxuHv3rnSf5ORkeHl5wcnJCdOmTcOTJ0+UFT4RETVwTDCJqNbK39bmF5YqOxQiqsHdu3cRERGBJUuW4OzZs/j4448xc+ZMiMViBAYGws/PD6mpqXB1dcXs2bMBADk5OQgNDcXixYtx8uRJ6OvrIywsTMlXQkREDRUTTCIiombi3Llz6NGjBzw8PKClpYXRo0fj+fPnOH78OExNTeHj4wNdXV0EBARAKBTi1q1bSEhIgLu7Ozw9PaGvr4+goCD88ssvyM/PV/blEBFRA8SG4URERM3E6NGj8dlnn0EikaCgoAD79+9Hp06dcP36ddjZ2Um3U1dXh5mZGYRCIbKystC1a1fpOiMjI2hqauLRo0fQ0dGp8jyxsbGIjY2VKdPQ0EBwcHDdXBgRETUYTDCJ6L297wh5RNQwqKmpQU1NDWfPnsWkSZMAAIGBgfjjjz+gp6cns62WlhbEYjFEIhG0tbUrrSsoKKj2PL6+vvD19ZUpKy0tRWZmpoKuhIiIGiq5Esxnz57hwoULuHLlCp4+fQpVVVW0bdsWNjY26NOnDzp06FDXcRJRA8IR8ogatz59+uDKlSu4dOkSAgMDoaenh48++khmm8LCQujq6kJHRwdFRUWV1lVXe0lERM1bjX0w79y5g4CAAHz00UdYt24dsrOzUVpaitevX+POnTvYunUrPDw84O/vj6ysrPqKmYiIiD7Ajh07cODAAQBvmsH27NkTzs7OGD16NG7cuCHdrri4GEKhEHZ2drC0tJRZl5ubi9evX8PMzKy+wyciokag2qqHyMhInDlzBj4+Pli6dGm1tZR5eXn46aefMG/ePDg5OWHlypV1FiwRERF9OAMDA2zZsgWOjo4wNzdHWloaMjIyMHv2bGzZsgVJSUno27cvoqOj0a1bNxgaGmLYsGEYMWIE0tPTYWFhgZUrV0IgEEBdXV3Zl0NERA1QtQmmk5MTFixYABUVlRoP0LZtW3z++ef4/PPPcezYMYUHSERERIrx6aefIicnBzNmzMCLFy9gZmaGyMhI2NjYYPPmzQgPD8f8+fPh4uKCqKgoAICZmRkiIiIQEhKCvLw89O/fH6GhoUq+EiIiaqiqTTCHDBlS7U6XL19GRkYG7O3t0bNnT2m5t7e3YqMjogaBg/oQNQ2qqqqYPXu2dI7Litzc3JCUlFTlfgKBAAKBoK7DIyKiJuC9R+f4/vvv8Y9//AMDBgxATEwMJk6ciIkTJ9ZBaETUUHBQHyIiqkt8kUnUdLz3k+I333yDtWvXwsnJCf3798e8efOYYBIRERHRB+OLTKKmo9q/4JUrV2LatGlo166dTHnLli3x8uVLAMDLly/RsmXLuo2QiIiIiJotndYtmnUNZ3O+dmqcqk0whw8fjq+++lFj/KQAACAASURBVArGxsaYOnUq2rZtCwCYOXMmQkJC4OLigvPnz2PRokX1FiwR1Q/ezIiIqCFpzjWczfnaqXGq9n+pnZ0dNm3ahMuXLyMsLAzm5ub44osv4OnpCWtra1y9ehVz5szhPFhETRBvZkRERPWvudfWUtPwzidHR0dHbN68Genp6ViwYAHs7OwwceJEDBo0qD7iIyIiIiJqNviSlxq7d/6vTUpKkk6u/PXXXyMtLQ3BwcHo3r07xo8fD01NzfqIk4iIiIiIiBo41ZpWRkdH4+uvv4aZmRmOHj2KOXPmwM3NDf/4xz9gY2ODoKAg7N69u75iJSIiIiIiogasxhrMgwcPYv369ejRoweGDx8OV1dXPH/+HPr6+nB3d4e7uztSUlLqK1YiqiPs70FEREREilBjDaa+vj7u3bsHALh37x5atmyJNm3ayGzj5eVVd9ERUb0o7+/xZ0EJ8gtLlR0OEVWjrKwMMTExuHjxIgBg1apV8PT0xJw5c/DixQslR0dERPSOBPPLL7/Exo0bMWrUKEyZMgXLli2Durp6fcVGREREFXz99dfYsWMHACA9PR379+/HuHHjkJOTg6ioKCVHR0RE9I4msj169MCxY8dw9+5dmJiYQE9Pr77iIiIiorckJiZi+fLlcHFxwerVq+Hl5YXJkyfDwcEBc+bMUXZ4RERE1ddgbt26FSKRCK1bt0bXrl3fmVzm5eVhw4YNCg+QiIiI3nj27Bk6d+4MADh//jzc3NwAAFpaWnj58qUyQyMiIgJQQw1mmzZtMGzYMLi7u+Pjjz+Go6MjtLS0ZLYRiUT47bffcOTIEZw/fx4zZsyo84CJqPY4qA9R42RnZ4f4+HhYW1vj5s2b+OijjwAAhw8fhrm5uZKjIyIiqiHBnDBhAv72t79h165dmDt3LvLz86Gvrw8dHR1IJBL8+eefyMvLg6GhIT755BP88MMP0NXVrc/YiegDcRJnosZpwYIFmDFjBr755htMnToVhoaGmDt3Lk6dOoUtW7YoOzwiIqKa+2Dq6upi7ty5mDNnDq5du4Zbt27h+fPnKC0thaGhIbp37w5TU9P6ipWIiKjZkkgkMDU1xc8//4yCggJoa2sDAPz9/REWFsZxEoiIqEGQq+pCRUUFXbt2RdeuXes6HiIiIqqGh4cHDhw4AGtra2mZlZWVEiMiImXRad1CpsuLbusWiF/grOSoiN4xTQkRERE1DCoqKhg+fDgOHjxYq+P88ssvEAgEcHR0xODBg5GQkADgTU2otbW19KdXr17SfZKTk+Hl5QUnJydMmzYNT548qVUMRKQYFeexLk80iZSNna+ImgEO6kPUNIhEIhw9ehSnT5+GpaUlVFRUZNZHR0fXuH9eXh6CgoKwYMECDB06FOfOncOcOXPQpUsXPHjwAMePH6/U9SUnJwehoaFYt24dunXrhtWrVyMsLAzbtm1T+PUREVHjxwSTqBngoD5ETUPLli0hEAg+eP/09HQYGxvD19cXAODt7Q0rKyucPXsWubm5MDY2rrRPQkIC3N3d4enpCQAICgrCgAEDkJ+fDx0dnQ+OhYiImqYG86SZm5uLxYsXIz09HXp6epg6dSrGjh2LtLQ0hIeH48GDB7C3t8eKFSuqHIpdLBZj0aJFSE1NhZ6eHmbMmAEfHx8lXAkREVHdiIyMrNX+Li4u2Lhxo3T5+fPnyMnJgb6+PlRUVDB27FhkZ2fD3NwcixYtgouLC7KysmTGYDAyMoKmpiYePXrEBJOogWMfTVIGufpglpWVISYmBhcvXgQArFq1Cp6enpgzZw5evHihkEDmzp0La2tr/Pzzz1i/fj1WrVqFrKwsBAYGws/PD6mpqXB1dcXs2bOr3D8yMhLFxcU4duwY1qxZg6ioKFy9elUhsRERETUUP//8M2bPng1fX1/897//RUxMDG7duiXXvvr6+rC0tATwpjZz7NixcHR0hKWlJSwsLDB//nykpqZi2LBh8Pf3x9OnTyESiaQj1pbT0tJCQUFBteeJjY3FiBEjZH7GjRv34RdNRB+EfTRJGeRKML/++mvs2LEDwJsb0v79+zFu3Djk5OQgKiqq1kHcunULDx8+xNy5c6GjowNnZ2fExsYiKysLpqam8PHxga6uLgICAiAUCivdSIuLi5GYmIjg4GC0a9cOPXr0wKBBg/Cf//yn1rERERE1FAkJCZg5cyZat26Na9eu4dWrV3jw4AFGjRqF06dPy3UMsViM+fPnY9q0afDx8cH27dvRtWtXHDhwAN27d0ebNm0wceJEdOjQARcvXoSOjg6KiopkjlFYWFhj7aWvry8OHTok87Nv375aXTsRETUOcjWRTUxMxPLly+Hi4oLVq1fDy8sLkydPhoODA+bMmVPrIDIzM2Fqaoq5c+fi5MmTaN++PQIDA5GdnQ07Ozvpdurq6jAzM4NQKESXLl2k5ffv30dZWZn0rSzwZtj2X3/9tdaxETVGHNSHqGnasWMHgoODMWHCBCQmJgIAwsLCYGBggA0bNqBfv3417l9UVISxY8fC0NAQSUlJ6NChA4A3taLFxcXw9vaWbltSUgItLS1YWlrixo0b0vLc3Fy8fv0aZmZmir9AIiJq9ORKMJ89e4bOnTsDAM6fP4+xY8cCeNNE5uXLl7UOIi8vD7/++iuWLl2KFStW4OLFiwgMDISLiwvs7e1lttXS0oJYLJYpE4lEld6kytN8JzY2VqZMQ0MDwcHBtbwaIuXjoD5ETZNQKJSZPqScl5eXtKVRTRISElBcXIwtW7ZAXV1dWl5cXIxly5ahffv2sLS0RHx8PIqLi9GzZ0+YmJhgxIgRSE9Ph4WFBVauXAmBQCCzPxERUTm5njzt7OwQHx8Pa2tr3Lx5Ex999BEA4PDhw1UOuPMhrKyspImru7s73NzccPr0aVhYWMhsV1hYCF1dXZkyXV1dFBYWvnO7inx9faWj6JUrLS1FZmZmbS6DiIioznTq1AnZ2dmwtraWKb927RratWv3zv1v3LiBe/fuwcHBQaY8MjISX3zxBYKCgiAWi2Fvb4+dO3dCQ0MDZmZmiIiIQEhICPLy8tC/f3+EhoYq9LqIqPZ0WrdgCyZqEORKMBcsWIAZM2bgm2++wdSpU2FoaIi5c+fi1KlT2LJlS62DMDExQUmJbMfj0tJSfPnll9ImQMCbN6xCoVCm2SwAGBsbo7S0FDk5OdL5u27fvg1bW9tax0ZERNRQzJo1C0uWLEFubi4kEglOnTqFgwcP4sCBA1i0aNE79w8LC0NYWFi16ydPnlxluUAgqNX0KERUP9iCiRqCdw7yI5FIYGpqip9//hlpaWmYN28eAMDf3x8nTpyAm5tbrYNwd3fH8+fP8d1336GgoACnTp3CpUuX4O7ujuzsbCQlJUEkEkkneTY0NJTZX1NTE4MHD8b69eshEolw5swZHD16lDdDIiJqUgYOHIitW7fi3Llz0NbWxtatW/Hbb79h3bp1nJqLiIgaBLlGkfXw8MCtW7dkhim3srKCnp6eQoJo06YN9uzZg59++gn9+vXDxo0bER0dDWNjY2zevBkbN25E3759cfPmTZk5wKytrXHhwgUAwMKFC/Hq1Sv07dsXS5cuxVdffSWtzSQiImoqXFxcsGvXLpw9exZpaWk4cOAAPD09lR0WERERADmayKqoqGD48OE4ePAglixZUmeB2NjY4LvvvqtU7ubmhqSkpCr3yc7Olv6up6eHrVu31ll8RA0Z+1wQNQ8lJSVITEzEnTt3UFxcXGn9woULlRAVERHR/8jVOFskEuHo0aM4ffo0LC0toaKiIrM+Ojq6ToIjIvmwzwVR8xAaGooTJ07A1tYWampqMuvevjcTEREpg1xPoi1btmR/RiIiIiVLSUlBdHT0O+e7JGro2PKGqOmSK8Gs2O+RiIiIlENfXx+tWrVSdhhE71QxgdRt3QLxC5xl1rPlTf17exqTqv5diBRB7r/on3/+GYcPH8bjx4+xYcMGJCcno2/fvujSpUtdxkdERET/b/bs2QgPD8esWbNgYmJSqVmsjY2NkiIjklUxgaSGg/8uVB/kSjATEhKwePFiCAQCXLt2Da9evcKDBw+wYcMGbNmyhU11iIiI6kFRURHu3buHmTNnSstUVFQgkUigoqKCGzduKDE6aspY80VE8pIrwdyxYweCg4MxYcIEJCYmAngzWbOBgQE2bNjABJOonrHvClHztH79eowfPx6+vr5o2bKlssOhZoQ1X0QkL7kSTKFQiF69elUq9/Lywo4dOxQeFBHVjH1XiJonVVVVDBs2DGZmZsoOhYiIqEqq8mzUqVMnmTkny127dg3t2rVTeFBERERU2RdffIHt27ejsLBQ2aEQERFVSa6qj1mzZmHJkiXIzc2FRCLBqVOncPDgQRw4cACLFi2q6xiJiIgIwE8//YTs7Gy4ubnByMio0oiyhw8fVlJkREREb8iVYA4cOBAGBgbYunUrtLW1sXXrVlhYWGDdunXw9PSs6xiJiIgIgIeHBzw8PJQdBhERUbXk7rzl4uKCXbt21WUsRFQNDupDRAAQGBio7BCIOJ8iEdVIrgSzpKQEiYmJuHPnDoqLiyutX7hwocIDI6L/4aA+RFTu6NGj2LlzJ+7du4dWrVrBxsYGU6ZMQe/evZUdGjUjHFWWiKoj15NqaGgoTpw4AVtbW6ipqcmse3uSZyIiIqobCQkJCAsLw7hx4zBp0iQUFhYiLS0NU6ZMwfLlyzF8+HBlh0hUyds1nmyJQ9S0yZVgpqSkIDo6mvNdEhERKdE///lPhIWF4dNPP5WWjRw5Era2ttixYwcTTGqw2BKHqPmQ6y9cX1+/0kh1REREVL+EQiHs7e0rlbu6umLt2rVKiIiImhL2rSVFkGsezNmzZyM8PBzHjh3DjRs3kJWVJfNDRIo1Kuo3eIelwTssDZM2XVZ2OETUQJiYmODMmTOVyjMyMtC+fXu5jvHLL79AIBDA0dERgwcPRkJCAgAgLS1NWj5mzBjcvXtXuk9ycjK8vLzg5OSEadOm4cmTJ4q5ICJqUMprmv8sKJEmmkTvS64azKKiIty7dw8zZ86UlqmoqEAikUBFRQU3btyoswCJmiM2JSKiqgQEBCAkJAQ3b96Eq6srWrRogczMTMTHx2PevHnv3D8vLw9BQUFYsGABhg4dinPnzmHOnDno0qULAgMDERwcjIEDB2L37t2YPXs2fvzxR+Tk5CA0NBTr1q1Dt27dsHr1aoSFhWHbtm31cMVERNTYyPXkun79eowfPx6+vr5o2bJlXcdEREREVRgyZAgMDAywfft2rF27FkVFRTA3N0dkZCSGDBnyzv3T09NhbGwMX19fAIC3tzesrKxw/PhxmJqawsfHB8CbRDYmJga3bt3CsWPH4O7uLp33OigoCAMGDEB+fj50dHTq7mKJiKhRkivBVFVVxbBhw2BmZlbH4RAREVF10tLS4OzsjN27d8uUv3r1CpcuXYKTk1ON+7u4uGDjxo3S5efPnyMnJwc//PAD+vTpIy1XV1eHmZkZhEIhsrKy0LVrV+k6IyMjaGpq4tGjR0wwmxD2vSMiRZErwfziiy+wfft2REVFQVNTs65jIiIiogrEYjEkEgkmTJiAw4cPw8TERGZ9ZmYmZsyYgcuXa+6zra+vD319fQBvajO//PJLODo6wsjIqFKyqKWlBbFYDJFIBG1t7UrrCgoKqj1PbGwsYmNjZco0NDQQHBz8zmsl5eC8lkSkKHIlmD/99BOys7Ph5uYGIyOjSiPKHj58uE6CIyIiIqBHjx7SsQ8qTlFSTiKRwMHBQa5jicViRERE4Pjx4wgICICfnx/Wr1+PoqIime0KCwuhq6sLHR2dKtfVVHvp6+srbYZbrrS0FJmZmXLFSLXHGkkiUha5EkwPDw94eHjUdSxEzRYnoCaimuzduxcSiQR+fn5YvXo1DA0NZdarq6vDzs7unccpKirC2LFjYWhoiKSkJHTo0AEAYGlpie+//166XXFxMYRCIezs7HD16lWZwfxyc3Px+vVrdptp4FgjSUTKIleCGRgYWNdxEDVrHDWWiGri6uoK4E2i2a1bN6irq0vXlZWVQVVVrlnHkJCQgOLiYmzZskXmGN7e3li5ciWSkpLQt29fREdHo1u3bjA0NMSwYcMwYsQIpKenw8LCAitXroRAIJDZn4iaHp3WLVgTTh9E7ifZo0ePYufOnbh37x5atWoFGxsbTJkyBb17967L+IiIiOj/OTo6YvXq1ejfvz/69euHWbNm4cSJE7C3t8eGDRtgbGxc4/43btzAvXv3KjWnjYyMxObNmxEeHo758+fDxcUFUVFRAAAzMzNEREQgJCQEeXl56N+/P0JDQ+vsGqnxeTsRYUucpoM14fQh5EowExISEBYWhnHjxmHSpEkoLCxEWloapkyZguXLl2P48OF1HScREVGzFxUVhXPnzmHUqFFITU3FhQsXsH79esTHx2PFihXYunVrjfuHhYUhLCys2vVJSUlVlgsEAggEglrFTk0bW+I0PnwxQHVFrm+Af/7znwgLC5MZWGDkyJGwtbXFjh07mGASERHVg1OnTiEyMhI2NjaIi4uDt7c3Bg0ahI4dO2L69OnKDo+aCCYezQdfDFBdkOt/klAohL29faVyV1dXrF27VuFBETV1vHET0YcQiURo3749AOD8+fPw9/cHAKioqKC0tFSZoVETw8SDiD6UXN8YJiYmOHPmDKysrGTKMzIypDc6IpIfb9xE9CG6d++Obdu2wdzcHDk5OejXrx/EYjFiYmJgY2Oj7PCIiIjkSzADAgIQEhKCmzdvwtXVFS1atEBmZibi4+Mxb968uo6RiIiIACxZsgTBwcFITU1FaGgo2rZti4CAAFy/fh1btmxRdnhERETyJZhDhgyBgYEBtm/fjrVr16KoqAjm5uaIjIzEkCFD6jpGIiIiAtCpUyccPHhQpiwiIgJ6enpQU1NTUlTUGLGrBhHVFbkSzLS0NDg7O2P37t0y5a9evcKlS5fg5ORUJ8ERERHR/6SkpNS43svLq54iocaOXTWIqK7U+I0iFoshkUgwYcIEHD58GCYmJjLrMzMzMWPGDFy+fLlOgyQiIqI3XVaqoqamBj09PSaYRESkdDUmmD169ICKigokEonMFCXlJBJJpcmaiagyNkUiIkXIysqSWS4uLsadO3ewadMmDBgwQElRUUPA+wwRNRQ1Jph79+6FRCKBn58fVq9eDUNDQ5n16urqsLOzq9MAiZoCNkUiorqgrq4OW1tbrFmzBiNGjMCoUaOUHRIpCe8zRNRQ1PgN5OrqCuBNotmtWzeoq6tL15WVlUFVVbVuoyMiIqJ3ev36NZ48eaLsMKiB0mndgjWcRFRv5HrF5ejoiNWrV6N///7o168fZs2ahRMnTsDe3h4bNmyAsbGxQoLJzc3FsGHDsGHDBvTp0wdpaWkIDw/HgwcPYG9vjxUrVsDc3LzSfmKxGIsWLUJqair09PQwY8YM+Pj4KCQmIiKihiIyMrJSWVFREc6fP48ePXooISJqLFjDSUT1Ra4qyKioKKSmpqJ9+/ZITU3FhQsXsH79eujq6mLFihUKC+bLL7+ESCQC8CZpDAwMhJ+fH1JTU+Hq6orZs2dXuV9kZCSKi4tx7NgxrFmzBlFRUbh69arC4iIiImoIrl+/Xunn4cOH8PDwwOrVq5UdHhERkXw1mKdOnUJkZCRsbGwQFxcHb29vDBo0CB07dsT06dMVEkhcXBxat26Njh07AngzFLupqam0JjIgIAAxMTG4desWunTpIt2vuLgYiYmJ+P7779GuXTu0a9cOgwYNwn/+8x907dpVIbERvS82RSIiRSorK0NiYiJCQkLg6OgI4M2L2A0bNmDQoEHSLi1ERETKJlcNpkgkQvv27QEA58+fR+/evQEAKioqKC0trXUQDx8+xI4dOxAWFiYty8rKkhlASF1dHWZmZhAKhTL73r9/H2VlZbC0tJSWWVlZVdqOqD6VN0X6s6AE+YW1/xshouaroKAA48ePx6JFi2T6WaqqquLy5cvw8/PDkiVLlBghERHR/8hVg9m9e3ds27YN5ubmyMnJQb9+/SAWixETEwMbG5taBSCRSLBw4UIEBwdDX19fWi4SiaCnpyezrZaWFsRisUyZSCSCjo5Ope0KCgpqPG9sbCxiY2NlyjQ0NBAcHPwhl0FERFQnNm3ahKdPn+LIkSMwNTWVlrdu3RpxcXHIyMjAzJkzYWlpCT8/PyVGSkREJGeCuWTJEgQHByM1NRWhoaFo27YtAgICcP36dWzZsqVWAezbtw8GBgYYNGiQTLmOjg6KiopkygoLC6GrqytTpquri8LCwndu9zZfX1/4+vrKlJWWliIzM/N9L4GIiKjOHD9+HCEhITLJZUXdu3dHcHAwdu/ezQSTiIiUTq4Es1OnTjh48KBMWUREBPT09KCmplarAM6dO4eUlBQcOXJEWjZp0iRoa2vD2tpaWlZcXAyhUFhp3k1jY2OUlpYiJydHevO9ffs2bG1taxUXERFRQ5Cbmysz9kBVHB0d2TWEiOrU29Pd6LZugfgFzkqOihoiuRLMlJSUGtd7eXl9cABbt26VWfb09MTy5cvh4OAADw8PJCUloW/fvoiOjka3bt1gaGgos72mpiYGDx6M9evXIyIiApcvX8bRo0cRHx//wTERERE1FHp6esjNzZUZa+BtT58+haam5nsdNzQ0FD169JAOpufv74+TJ0/KnPfChQsAgOTkZKxatQpPnz5Fr169sGLFCunYDM0JH66puas43Q1RdeRKMAMCAqosV1NTg56eXq0SzOpoa2tj8+bNCA8Px/z58+Hi4oKoqCjpemtra+zduxe9evXCwoULsWjRIvTt2xcdOnTAV199VW1TIqK6wFFjiaiuuLu7Y/v27ejduzdUVasem2/Pnj3o06ePXMc7ffo0UlNT8eOPP8rMnfngwQMcP3680v0zJycHoaGhWLduHbp164bVq1cjLCwM27Zt+/CLaqT4cE30P6zRpOrIlWBmZWXJLBcXF+POnTvYtGkTBgwYoNCATpw4If3dzc0NSUlJVW6XnZ0t/V1PT69STShRfeIE1kRUV4KCgjBy5EiMGzcO06dPh6OjI3R1dfHs2TNcv34dO3fuRHZ2NuLi4uQ6XkZGBsRiMQwMDKRlEokEubm5MDY2rrR9QkIC3N3d4enpKY1nwIAByM/PrzTIHhE1L3zpQlX5oCdhdXV12NraYs2aNRgxYgRGjRql6LiIiIgIgKGhIfbv349ly5Zh+vTpUFFRka6TSCTo2bMn/vWvf6Fz585yHW/WrFkAgPHjx0vLfv/9d6ioqGDs2LHIzs6Gubk5Fi1aBBcXF2RlZcnMK21kZARNTU08evSICWYdY+0QETVGtapqef36tcycXERERKR4pqam2LVrFx4/fozr16/j5cuX0NbWhq2tbaWxCT7E8+fPYWFhgfnz58PKygrx8fHw9/fHkSNHIBKJoK2tLbP9u6YD41RgisHaISJqjORKMCMjIyuVFRUV4fz58zL9N4iak/I3y+xzSUT1xcjICEZGRgo/rr29PQ4cOCBdnjhxIuLi4nDx4sVqpw2rqfaSU4ERETVfciWY169fr1Smrq4ODw8PTJs2TeFBETUG5W+W2eeSiBq7n3/+GcXFxfD29paWlZSUQEtLC5aWlrhx44a0PDc3F69fv4aZmZkSIm2+OKAKETUWcj0Zf/vtt3UdBxERESlJcXExli1bhvbt28PS0hLx8fEoLi5Gz549YWJighEjRiA9PR0WFhZYuXIlBAIB1NXVlR12s8Mms0TUGNSYYJaVlSExMRFmZmZwdHQEAIjFYmzcuBGDBg1Cz5496yVIIiIiqjve3t7IyclBUFAQxGIx7O3tsXPnTmhoaMDMzAwREREICQlBXl4e+vfvj9DQUGWHTEREDVS1CWZBQQGmTZuGS5cuYdOmTdJyVVVVZGZm4rvvvsOoUaPw1Vdf1UugREREpDhvt06aPHkyJk+eXOW2AoEAAoGgPsIiokaKzbipXLUJ5qZNm/D06VMcOXJEZtLl1q1bIz4+HhkZGZg5cyYsLS3h5+dXL8ESKVPFL00O7ENE1PC978Pu+2xfFw/TvM9QY8dm3ATUkGAeP34cISEhMsllRd27d0dwcDB2797NBJOahYpfmhzYh4io4Xvfh93abK+IhJP3GSJqCqr99srNzUWXLl1q3NnR0RFCoVDhQRERERG9y9s1fqLCUoXVAL6dMMpzPNbeEP0Pm8w2X9UmmHp6esjNzYWlpWW1Oz99+hSampp1EhgRERFRTd6u8ctXcA0gaxSJaocvXZqnar8t3d3dsX37dvTu3RuqqqpVbrNnzx706dOnzoIjUib2hSEiIiIiej/VJphBQUEYOXIkxo0bh+nTp8PR0RG6urp49uwZrl+/jp07dyI7OxtxcXH1GS9RveGbayKipqMhNNdT9vmJiOpDtU/NhoaG2L9/P5YtW4bp06dDRUVFuk4ikaBnz57417/+hc6dO9dLoERERES1UZ/N9arqw8nmgkTUHNRYLWNqaopdu3bh8ePHuH79Ol6+fAltbW3Y2trC0NCwvmIkIiIiUqgPGcTnfbElDBE1R3J92xkZGcHIyKiuYyFSKva5JCJqXpgAEhEpHr9Nif4fHzSIiIiIiGqn6uFhiYiIiIiIiN4Tq2mo2WKTWCIiIiIixWKCSc0Wm8QSEVFjVXGQIr4kpcZAnqmCOJVP08CnaiIiIqJGqPxFKV+SUmPx9st9TuXTNPEbiZoNNoklIiIiajjYmqxp4r8kNRv8EiMiooaiPubhJCJSBj5lExERESkBX3wSUVPEbzNqsvhmmIiIiIiofnEeTGqyyt8M/1lQgvzCUmWHQ0TUoISGhiIuLk66nJaWBoFAAEdHR4wZMwZ3796VrktOToaXlxecnJwwbdo0PHnyRBkhExFRI8AEk4iIqBk5ffo0IiMjx+hQSgAAIABJREFU8eOPP0rLxGIxAgMD4efnh9TUVLi6umL27NkAgJycHISGhmLx4sU4efIk9PX1ERYWpqzwiaiZKO+n7B2WBu+wNIyK+k3ZIZGc2ESWmgw2iSUiereMjAyIxWIYGBhIy1JSUmBqagofHx8AQEBAAGJiYnDr1i0cO3YM7u7u8PT0BAAEBQVhwIAByM/Ph46OjlKugYiaB05b0jgxwaQmg4MlEBG926xZswAA48ePl5ZlZWXBzs5Ouqyurg4zMzMIhUJkZWWha9eu0nVGRkbQ1NTEo0ePmGASUb2pOPKybusWiF/grOyQqBp8CiciImrmRCIR9PT0ZMq0tLQgFoshEomgra1daV1BQUG1x4uNjUVsbKxMmYaGBoKDgxUXNBE1O6zRbByYYBIRETVzOjo6KCoqkikrLCyErq5utev+j707j4uq3P8A/mHfZIZNUBEjRfbVXTEUi0TFTLuK69W83vSmF0sxtfy5ltRNyb0yra56r5FbiiYJmmZpiBniwqbGgKihsjiDAwMz8/vD6+QEIsKs8Hm/XrzynHnmnO95Ar5853nOcxoavYyNjUVsbKzaPrlcjszMTM0FTUREBokFJhkt3nNJRKQZXl5e2LNnj2pbJpNBJBLB398fFy9eRHZ2tuq1kpIS1NTUwNPTUw+REhGRoeMqsmS0+BgSIiLNiIqKQm5uLlJSUiAWi7F69WqEhobCzc0Nw4cPR1paGs6ePYuysjKsXLkSMTExsLS01HfYRERkgFhgEhERtXL29vZYv3491qxZg/DwcOTl5SEhIQEA4OnpieXLl2PevHmIjIyEUqnE/Pnz9RwxEREZKk6RJaPBKbFERJqzfft2te2+ffsiJSWl3rYxMTGIiYnRRVhERGTkDGIE84cffkBMTAyCg4MRHR2N5ORkAEBGRoZq/7hx43Dt2rV63y+RSBAXF4ewsDBERkZi165dugyfdIRTYomIiIiIDJveC8yysjLMnj0bkyZNwqlTpzB37lwsXLgQOTk5mDVrFiZPnoyTJ0+iV69eeOONN+o9RkJCAmQyGVJTU/Hhhx/i/fffx8WLF3V8JURERERERK2b3qfInj17Fu7u7qrlzKOiouDt7Y20tDR4eHhg9OjRAICZM2fiyy+/RH5+Prp27ap6v0wmw8GDB7Fnzx64uLjAxcUFgwcPxqFDh9QeDE3Gh1NiiYiIiIiMi94LzO7du2PNmjWq7dLSUhQVFeGbb75Bv379VPstLS3h6ekJkUikVmAWFBRAoVDAy8tLtc/b2xtnzpzRzQWQ1jz6MF2Brd6/VYmIiIjIAAhszdUGIoS25ti9IEzPUdFDev+r3cnJCU5OTgAejGYuWrQIwcHBaN++fZ2HONvZ2UEikajtE4vF9barrKxs8LxJSUlISkpS22dlZYX4+PimXgoREREREenAowMRZFj0XmACDxbpWb58OdLS0jBz5kxMnjwZiYmJqKqqUmsnlUohFArV9gmFQkil0ie2+7PY2FjVtNyH5HI5MjMzm3El1BycEktEREREZNz0XmBWVVVh/PjxcHNzQ0pKClxdXQEAXl5e2LNnj6qdTCaDSCSCv7+/2vvd3d0hl8tRVFQEDw8PAMCVK1fg5+enu4sgjeCUWCIiIiIi46b3v+KTk5Mhk8mwceNGWFpaqvZHRUVh5cqVSElJQXh4ODZs2IDQ0FC4ubmpvd/GxgbR0dFITEzE8uXLkZWVhe+++w67d+/W9aXQU+KIJRERERFRy6L3x5RkZ2fjt99+Q1BQEHx8fFRfqampWL9+PdasWYPw8HDk5eUhISFB9T4fHx+kp6cDABYuXIjq6mqEh4djyZIlWLFihWo0kwwXn2tJRERERNSy6H0Ec/HixVi8ePFjX09JSal3f25ururfDg4O2LRpk8ZjIyIiIiIiosbTe4FJrQenxBIRUXM9zCXMI0T0EB9bYlhYYJLOcBEfIiJqroe5hHmEiB7Fx5YYDr3fg0lEREREREQtAz/+I63hlFgiIiIiMgScQqs7LDBJazglloiIiIgMwZ//LmXBqT38q580hiOWRERERGQMeM+m9rDAJI3hiCURERERUevGKoCahcvFExG1HDNmzMD333+v2nZwcEB6ejqOHDmCDz74AHfu3EHv3r3x3nvvoW3btnqMlIiIDBVXkaVmeThqeU8q13coRETUTIWFhUhLS0Nubi5yc3ORnp6OoqIizJ8/H++88w6+//57ODk5YfHixfoOlYiIDBRHMOmp8D5LIqKWSalUoqSkBO7u7mr7k5OTERERgUGDBgEAZs+ejRdeeAH37t2DQCDQR6hERA368yI+/JtVt1hg0lPhfZZERC3TrVu3YGJigvHjxyM3NxedO3fG22+/jZycHAQGBqratW/fHjY2NiguLmaBSUQG62n+Zq2vIBVL5VxltolYIVCD+OkPEVHrUFpaii5duuCtt96Ct7c3du/ejRkzZsDX1xf29vZqbe3s7FBZWfnYYyUlJSEpKUltn5WVFeLj47USOxFRc/25IL3HVWabjAUmNYgjlkRErUNAQAC++uor1faUKVOwa9cunDt3TjU99iGpVNrg6GVsbCxiY2PV9snlcmRmZmo2aCIiMjisGEgNRyyJiFqnEydOQCaTISoqSrWvtrYWQUFByM7OVu0rKSlBTU0NPD099RAlEZHu/XkKLafMNowFJqnhiCURUeskk8mwdOlStG3bFl5eXti9ezdkMhmWLVuGcePG4ezZs+jSpQtWrlyJmJgYWFpa6jtkIiKdqeCU2UZjBdHKccSSiIgAICoqCkVFRZg9ezYkEgkCAgKwZcsWdOnSBcuXL8e8efNQVlaGAQMGYP78+foOl4iIDBQLzFaOI5ZERPTQ1KlTMXXq1Dr7Y2JiEBMTo4eIiIjI2LCiaGU4YklERERE1HS8J7NhLDBbGY5YEhERERE1D+/JfDxWGC0cRyyJiIiIiEhXWGC2cByxJCIiIiIiXWHF0cJwxJKIiIiISL9a8z2aLDBbGI5YEhERERHpV2u+R5MVCBERERERURP9eVXZ1j6LkAWmkeM3MxERERGRfnEW4R9a99W3APxmJiIiIiIiQ8GKxMhwxJKIiIiIiAwVC0wjwxFLIiIiIiLj8ed7NFv6qrKsUAwcRyyJiIiIiIxba1pVlgWmgeOIJRERERERGQtWLAaGI5ZERERERGSsWGAaGI5YEhERERGRsWIFo2ccsSQiIiIiat2edhGgh+0NccEgFph6xhFLIiIiIqLW7WkXATLkRYNY0egYRyyJiIiIiKilMtV3AE+SkZGBmJgYBAcHY9y4cbh27VqdNhKJBHFxcQgLC0NkZCR27dqlh0gb5+GnDRWVtbgnles7HCIiokZpTD4mIqIne/hczKjFGYhanIFX12bpOySNMugRTIlEglmzZiE+Ph4vvvgiPv/8c7zxxhs4cOCAWruEhATIZDKkpqaioKAA06dPh5+fHwIDA/UU+R84YklERMausfmYiIgap6Hb5B4WoI/WEGKpvN6a4s9tDeGeTIMewTx69Cg8PDwwevRoCIVCzJw5EyKRCPn5+ao2MpkMBw8eRHx8PFxcXNCjRw8MHjwYhw4d0mPkf+CIJRERGbvG5GMiItKcP9cQDdUUj772sNDUJ4MewczJyYG/v79q29LSEp6enhCJROjatSsAoKCgAAqFAl5eXqp23t7eOHPmzFOfT6lUAgDk8qYXgrM+uQSx9MH/2HYOVnAVmMHqf73s0sYU1uYtc9uQYuG1sy947YZ97UJbs2b9ngX++D398Pc2aVdj8vGTaCLHAlDlVUP6GdD3tiHFwmvntfPa9dsXhpBjzZt1di0Ti8VwcHBQ22dnZweJRKLWRiAQ1GlTWVnZ4LGTkpKQlJSkts/e3h5xcXG4cOFCk2Oe1ufRrar//ffhQHFtC942pFh0vW1Iseh725Bi0fW2IcWi6+2nfa8SmZmZ0ASFQqGR41DDGpOPH6WtHAsAcyIB/sz9eduQYtH1tiHFouttQ4pF19uGFIu+tw0vxxp0gSkQCFBVVaW2TyqVQigUqraFQiGkUmmDbeoTGxuL2NhYtX0KhQK1tbUwNTWFiYlJM6Ov38SJE7Fjxw6tHLs1YP81D/uvedh/TafpvlMqlVAoFDA3N+g01mI0Jh8/Sl85FuDPaXOw75qH/dc87L+mM7Qca9CZ2cvLC3v27FFty2QyiEQitWk67u7ukMvlKCoqgoeHBwDgypUr8PPze+rzmZqawtLSsvmBN6C6uhpmZmZaPUdLxv5rHvZf87D/mo59Z9wak4+fRBc5FuD3WnOw75qH/dc87L+mM7S+M31yE/2JiopCbm4uUlJSIBaLsXr1aoSGhsLNzU3VxsbGBtHR0UhMTIRYLMZPP/2E7777DjExMXqMnIiIqOVoTD4mIiICDLzAtLe3x/r167FmzRqEh4cjLy8PCQkJAAAfHx+kp6cDABYuXIjq6mqEh4djyZIlWLFihWo0k4iIiJqnoXxMRET0KIOeIgsAffv2RUpKSp39ubm5qn87ODhg06ZNugyLiIioVXlcPiYiInqU2dKlS5fqO4jWJjAwUN8hGDX2X/Ow/5qH/dd07DvSFX6vNR37rnnYf83D/ms6Q+o7EyUfIkZEREREREQaYND3YBIREREREZHxYIFJREREREREGsECk4iIiIiIiDSCBSYRERERERFpBAtMLfrhhx8QExOD4OBgREdHIzk5GQCQkZGh2j9u3Dhcu3ZNz5EatpKSEvTu3RunTp0CwP5rjJKSEvz9739HWFgYIiMj8d///hcA+66x9uzZg6ioKAQHByMmJgZHjx4FwP57kvnz52PXrl2q7Yb668iRI3j++ecREhKC1157Dbdv39ZHyGTEmGM1gzm2aZhnm445tmmMKscqSStKS0uVoaGhyq+++kopFouVR44cUQYEBCizs7OVvXr1Un799dfK8vJyZWJionL48OH6Dteg/f3vf1f6+fkpf/rpJ6VYLGb/NcKECROUH374obKiokJ57tw5ZXBwML/3GqmgoEAZFBSkzMzMVFZXVysPHTqkDAgI4PdeA06ePKlcuXKl0tfXV/n1118rlUplg/1VWFioDA0NVR49elR59+5d5fz585UzZszQ5yWQkWGO1Rzm2KZhnm0a5tinZ4w5liOYWnL27Fm4u7sjNjYWbdq0QVRUFLy9vZGWlgYPDw+MHj0aQqEQM2fOhEgkQn5+vr5DNki7du2Cra0t2rVrBwA4evQo++8J8vPzcf36dcyZMwcCgQBhYWFISkpCTk4O+64RTE1NYWZmBuX/nuCkVCrRpk0bpKamsv8e49y5c5BIJHB2dlbta+hnNTk5GRERERg0aBCcnJwwe/Zs/PDDD7h3754er4KMCXOsZjDHNg3zbNMxxz49Y8yxLDC1pHv37lizZo1qu7S0FEVFRfjmm2/g7++v2m9paQlPT0+IRCJ9hGnQrl+/js2bN2Px4sWqfTk5Oey/J8jMzISHhwfmzJmDkJAQvPDCC8jJyUFubi77rhE8PDzw6quvIjY2FkFBQZgzZw4WLlyIvLw89t9jxMXF4b333sOzzz6r2tfQz2pOTg4CAgJUr7Vv3x42NjYoLi7WadxkvJhjm485tumYZ5uOOfbpGWOOZYGpJU5OTvDy8gLw4JPW8ePHIzg4GH369IFAIFBra2dnB4lEoo8wDZZSqcTChQsRHx8PJycn1X6xWMz+e4KysjKcOXMGvXr1wqlTp7B48WIsXrwYOTk57LtGOHv2LLZt24Zt27bh3LlzWLRoEZYsWcLvvafUUH+JxWLY29vXea2yslKXIZIRY45tHubY5mGebTrmWM0w9BzLAlOLJBIJ3nrrLbz22msYPXo0Pv30UwgEAlRVVam1k0qlEAqFeorSMO3YsQPOzs4YPHiw2n72X+N4e3tj/PjxsLOzQ0REBPr27YszZ86w7xohJSUFQ4cORe/evWFnZ4dJkyahQ4cOOHXqFPvvKTT0s/q41/6cLIkawhzbdMyxzcc82zTMsZph6DnWXGdnamWqqqowfvx4uLm5ISUlBa6urgAALy8v7NmzR9VOJpNBJBKpDXMTcPr0aRw9ehSHDx9W7Xv11Vdhb28PHx8f1T72X10dO3ZEbW2t2j65XI5Fixbh4MGDqn3su/pZW1tDJpOp7TMzM8O0adNw6NAh1T72X8Ma+l138eJFZGdnq14rKSlBTU0NPD099RApGSPm2OZhjm0e5tmmY47VDEPPsRzB1JLk5GTIZDJs3LhRlfgAICoqCrm5uUhJSYFYLMbq1asRGhoKNzc3PUZreDZt2oTc3FzVl7u7O7744gt8//337L8niIiIQGlpKf7zn/+gsrISx48fx/nz5xEREcG+a4RBgwbh8OHDSE9Ph1Qqxd69e1FWVobhw4ez/55CQ7/rhg8fjrS0NJw9exZlZWVYuXIlYmJiYGlpqe+wyUgwxzYPc2zzMM82HXOsZhh8jtXpmrWtyLJly5Te3t51vvbs2aM8deqUcvDgwcqgoCDllClTlLdu3dJ3uAYvMjJS+dNPPymVSiX7rxGys7OV48ePV4aGhipHjBih/Pnnn5VKJfuusQ4ePKgcMmSIMjQ0VDlmzBjlxYsXlUol++9JJk6cqFpCXalsuL+Sk5OVAwcOVIaEhCjj4uKUYrFYHyGTkWKO1Szm2KfHPNt0zLFNY0w51kSp/N86wURERERERETNwCmyREREREREpBEsMImIiIiIiEgjWGASERERERGRRrDAJCIiIiIiIo1ggUlEREREREQawQKTiIiIiIiINIIFJhEREREREWkEC0wiIiIiIiLSCBaYRISamhp9h0BERNRiMc9Sa8ICk8iAxcfHY8yYMWr7Nm7ciIiICCgUCmzYsAEREREICQnBX//6V1y4cEHVrqysDHPmzEHv3r0RFhaGsWPHIiMjQ/X6oEGDsHnzZkyZMgWjR48GACQnJ2PIkCEIDQ3F8OHD8e233+rmQomIiPSAeZZI80yUSqVS30EQUf2OHz+OGTNm4MSJE3BzcwMAvPzyy+jTpw8cHBxw8OBBLFiwAAKBALt378bBgweRkpICV1dXzJs3D4WFhZg/fz6srKywZcsWnD59GqdPn4aJiQkGDRqEsrIyxMfHo3///qipqcFLL72EhIQE+Pn5IT09He+++y52796NoKAgPfcEERGR5jHPEmmeub4DIKLHCw8Ph1AoRFpaGiZMmICioiJkZ2dj2bJlePXVV/HFF18gJCQEABAUFIRTp04hNTUVEyZMQGhoKP72t7/B19cXADBmzBh8++23kEgksLe3BwAMGTIEEyZMAACkpaUBAMLCwtCpUyd4e3vDxcVF1ZaIiKilYZ4l0jwWmEQGzMLCAi+++KIq8aWmpqJTp06ws7NDZWUlJk6cqNa+trYWxcXFAIC//OUvSE5Oxs6dO3Hz5k1kZ2cDAB6dtODt7a36d79+/dCrVy8MGzYM3bt3R58+ffDiiy/C09NT+xdKRESkB8yzRJrHApPIwA0bNgx/+9vfUFFRgdTUVAwdOlS1WMDWrVvh7Oys1t7BwQE1NTUYN24cTE1NMWrUKERHR6OqqgozZsx47HlsbW3x5ZdfIicnBz/++CN++uknrF27FuvXr8cLL7yg1WskIiLSF+ZZIs1igUlk4Hr16gVHR0fs2rULmZmZWLFiBTp06ABzc3NIJBL06tULAFBdXY3XXnsNr7/+Ouzt7XHp0iWkpaXBw8MDALB3794Gz3Pw4EGIRCLMnDkTvr6+mDZtGqZPn44jR44w8RERUYvFPEukWSwwiQycqakphgwZgg0bNsDX1xdeXl4AgIkTJyIhIQHm5uZwcHDA1q1bUVxcjKCgIEgkEpibm2Pfvn2Ijo7GhQsX8MknnwAArly5gm7dutU5j4ODAxYuXAgnJyd069YNIpEImZmZiIuL0+n1EhER6RLzLJFm8TElREYgJiYGUqkUI0eOVO2bO3cuBg4ciHnz5mHy5MmorKzEli1bYGtrC1dXV7z33nvYs2cPYmNjcfz4cXz22Wfw9fXFokWL6j1H//79MXfuXHz++ecYPXo03n//fUyYMAHjxo3T1WUSERHpBfMskebwMSVERiArKwsTJ07EiRMn4OjoqO9wiIiIWhTmWSLN4RRZIgNWXV2NK1euYMOGDRg2bBiTHhERkQYxzxJpHqfIEhmw0tJSxMbGorS0FLNnz9Z3OERERC0K8yyR5nGKLBEREREREWkERzCJiIiIiIhII1hgEhERERERkUawwCQiIiIiIiKNYIFJREREREREGsECk4iIiIiIiDSCBSYRERERERFpBAtMIiIiIiIi0ggWmERERERERKQRLDCJiIiIiIhII1hgEhERERERkUawwCQiIiIiIiKNYIFJpGGDBg2Cj4+P6iswMBAjRozAN998o7MYFixYgNdff13r57l+/Tp8fHyQnZ2t9XMREZHxaEm5MD09HT4+PhqI6A+1tbX44osvEBMTg6CgIPTu3RvTpk3Djz/+qNHz1MfHxwfp6elaPw+1Xub6DoCoJZo0aRImTZoEAKisrMTx48exaNEilJeXY8qUKVo//7x586BQKAA8SIx//etfkZGRAYFAoPVzN2TSpEnw9fXFO++8o9c4iIhI+5gL66dUKvHGG28gMzMTs2fPRmhoKMrLy7F//3787W9/Q2JiIoYNG6bXGImagwUmkRYIhUI888wzqm1/f39YWFggMTERMTExcHFx0er5nZ2dtXp8IiKiJ2EurN/XX3+NU6dO4ZtvvkGnTp1U+3v27AmlUokPP/wQQ4cOhYmJiR6jrKumpgYWFhb6DoOMAKfIEunImDFjoFAokJaWptq3c+dOvPDCCwgKCsJf/vIXtakxgwYNwtatWxEXF4eQkBD0798fu3fvVr1+4cIFjB8/HmFhYXj++efx0UcfQS6XA/hjWtDDT2yBB4nr2LFjCAkJwc6dO9Vii4qKQmJiYp2YH04L+v777zFs2DB069YNr7/+Om7fvl3vNZaWlmLu3Lno0aMH+vbti7lz56raTpo0CWfOnMG2bdtUn2gTEVHrYoy58KHk5GQMGjQIPXr0wOuvv47ff/8dADBu3Di8+eabam3ffvttjB07tt7j7Ny5E6+88opacflQXFwc5s2bB5lMppH+KS8vV70WERGBPXv2qJ1PoVBgw4YNiIiIQEhICP7617/iwoULasffvHkzpkyZgtGjRz+2b4gexQKTSEeEQiHatWuHK1euAHiQqD755BPMnz8fSUlJ6N+/P2bMmIHLly+r3rN+/Xr06NEDSUlJGDhwIJYuXQqxWAylUol//OMfCAwMxNdff42FCxciKSkJ//73v9XOGRISgg8//BAAsG/fPvTr1w8DBgxQS+w5OTkoLCxscDrO2rVrsWTJEmzduhV37txBXFxcve1mz56NgoICbNy4EZ9++ikkEgmmTZuG2tparFq1CsHBwRg1ahRWrVrV5H4kIiLjZcy5cPPmzVi+fDk++eQT3Lx5E7NmzQIAxMTE4MSJE6qiUC6X49ixY/Ueq6qqCjk5OejevXu953Bzc8OwYcNgZWXV7P4BgMWLFyM/Px+bNm3Chx9+iG3bttW5ppSUFKxcuRLbt2+Hp6cnJk+ejJKSElWbjz/+GFFRUVi7du1j+4boUZwiS6RDLi4uqgS0YcMGzJ8/H1FRUQAeTB06d+4ckpOT4e/vDwB4/vnnVZ+6vv7669i1axd+++03eHp64vbt2/Dz80PXrl3RtWtXODg41DmftbU13NzcAAAdO3aEtbU1hg0bhrlz5+LevXsQCAQ4cuQIvLy8GlzAID4+Hr169QIALF++HCNGjEB2djbs7e1VbX755RdkZGTgyJEjqk9lV61ahX79+uHHH3/EwIEDYW1tjTZt2qhiIiKi1sdYc2FCQgICAwMBAO+++y5GjRqFy5cvIzo6Gu+99x5Onz6NAQMGICMjAxUVFYiOjq5zjIqKCiiVSrX8mZGRgalTp6q1W7t2LQYNGtSs/nFxcUFqaip27tyJ0NBQAA9y+JgxYwAAMpkMmzdvxhdffIGQkBAAQFBQEE6dOoXU1FRMmDABADBkyBDVv4kagwUmkQ7du3cPjo6OuH//PkQiEd566y3Mnz9f9bpcLoejo6NqOyAgQPXvNm3aAACkUikEAgGmTZuGd955B9u3b0fv3r0xaNAg9OzZ84kxDBw4EJaWljh+/DheeuklpKamPnExgeDgYNW/fX19YWlpCZFIpEq0AJCbm4v27durTfmxt7eHu7s7fvvtNwwcOPCJsRERUctnjLnQwsJCVdABf9xPWlRUBH9/f/Tp0wepqakYMGAAUlNT0bt3b7Rt27bOcWxsbAAAd+7cUe0LDAxUra5bW1uLl156CQqFotn9k5ubC1NTUwQFBam1NTMzAwAUFhaisrISEydOVIuxtrYWxcXFqm1vb+/H9gtRfVhgEumIWCxGQUEBAgMDUVtbC6VSiYSEBLWEBfyRHADA1PTxs9jnzZuHiRMn4scff8Tp06cxbdo0jB07FgsXLmwwDisrK0RFRSEtLQ0hISHIy8vDhg0bGnzPozf119bWora2tk5s1dXVMDev+yulqqoK1tbWDR6fiIhaB2POhY/GoVAooFAoVPlt2LBhWL16NeRyOdLS0lTTZ/9MIBDAzc0Nly9fxogRIwA8KDq7dOkCALh69aqqbXP7p7a2FgDUFgt6GDfwYNEeANi6dWudBZHqGwkmaizeg0mkIzt27ECbNm0QHh4OgUCAtm3bory8HF26dFF9rV27FhkZGU88Vm5uLt588020a9cOo0ePRmJiIuLi4vDdd981KpahQ4fi5MmTSE5ORlBQkNoqf/XJyclR/fv8+fNQKBTw8vJSa9O5c2fcuHFD7b6NoqIi3Lx5E76+vo2Ki4iIWjZjzYU1NTW4dOmSajsrKwtKpVI1uhcVFYV79+7h3//+N+7evYsXX3zxscc8c1JqAAAgAElEQVQaPnw49u7di/Ly8jqv7dixQ/Xv5vZP586dUVtbq7Zoz/nz56FUKgEAzzzzDMzNzSGRSFTH7tixI5YvX666R5aoKTiCSaQFFRUVEIlEAB5MU0lLS8PHH3+MpUuXwtbWFgDw2muvYdOmTWjbti08PDywZ88e/Pzzz416RqSrqyuOHz+O9957DyNHjsT9+/fx7bffqk1lfcjS0hLAg6TSs2dPWFtbIzw8HFZWVtiyZQvmzJnzxPOtWLECb7/9Nmpra7F8+XL06dMHnTt3xvXr11Vt+vfvD09PT8ydOxezZ8+GQqFAYmIiunfvjrCwMFUsD4vO9u3bP7kjiYjIaLWkXGhqaoqlS5di/vz5UCgUePfddxEdHa3KZQKBABEREVi3bh0GDhwIoVD42GPNmDEDR48excSJEzFz5kx4eXmhpKQEu3btQlZWltroYXP6p0uXLhg4cCAWLVqEt99+G6ampli5cqVqVpKtrS0mTpyIhIQEmJubw8HBAVu3bkVxcbHatFqip8UCk0gLtm/fju3btwN4MA2nS5cuqudaPTRp0iRIJBK8++67EIvFCAgIwJYtWxq1AI6joyM2bNiA1atXY8+ePbCxsUF4eHi9U4L8/f0RFBSEf/zjH9i+fTvCwsJgbm6OwYMHY8+ePYiJiXni+caOHYu33noL5eXliIiIwOLFi+u0MTMzwyeffILly5dj6tSpsLCwQP/+/dWS4PDhw7Fs2TIsWbIEmzdvfuJ5iYjIeLWkXGhnZ4exY8fizTffhFgsRmRkJJYtW6bWJiYmBkePHsXIkSMbPJa9vT2SkpKwceNGfPDBB7hz5w6cnJwQERGBpKQk/N///Z9G+gd4sDDRokWLMH36dLRt2xZvvfWWWg6fO3cuFAqF6tEo3bt3x5YtW1QfABA1hYny4Tg5EbUqS5cuRWlpKdatW/fYNg+fHZaRkQGBQKDD6IiIiLSvMbmwsb799lusXLkSx48fr3dNAqLWgt/9RK3MzZs3UVhYiAMHDuDTTz/VdzhEREQ6p8lceO/ePRQWFmLLli0YO3Ysi0tq9bjID1Ers3//fsyYMQOvvPJKo5ZyJyIiamk0mQvz8/MxduxYCIVCTJkyRTMBEhkxTpElIiIiIiIijdDpCGZGRgZiYmIQHByMcePG4dq1a3XaSCQSxMXFISwsDJGRkdi1a5fqtcLCQkyePBkhISEYMGAAVq1aBblcDgD44osv4OPjo/aVl5ens2sjIiIiIiJq7XQ2SVwikWDWrFmIj4/Hiy++iM8//xxvvPEGDhw4oNYuISEBMpkMqampKCgowPTp0+Hn54fAwEDEx8fDx8cHH330EUpKSvDaa6+hffv2mDBhAkQiEVauXIlXXnmlyTEqFArVA+QffSgtEREZFqVSCYVCAXNz8wYfwk6GgzmWiMg4NDfH6qzAPHr0KDw8PDB69GgAwMyZM/Hll18iPz8fXbt2BQDIZDIcPHgQe/bsgYuLC1xcXDB48GAcOnQIHh4eOH/+PD755BM4OTnByclJ9YDcCRMmoKioCMOHD29WjH9+GC0RERm2oKAg1fPtyLAxxxIRGZem5lidFZg5OTnw9/dXbVtaWsLT0xMikUhVYBYUFEChUMDLy0vVztvbG2fOnIGNjQ2SkpLg5OQE4EFlfeHCBXh7ewMARCIR1q1bh0uXLsHBwQHTp09XFbON9bBCDwoKgpmZWbOul4iItEcul+PChQscvTQizLFERMahuTlWZwWmWCyGg4OD2j47OztIJBK1Nn9+1p6dnR0qKythaWmJ0NBQAEBxcTGWLVuGGzduIDExEQqFAvb29hg5ciQ2btyIrKwszJo1C+3atcNzzz1XbzxJSUlISkpS22dlZYX4+HiYmZkx+RERGQFOtTQeD/9fMccSERmHpuZYnRWYAoEAVVVVavukUimEQqFqWygUQiqVPraNQqHAli1b8PHHH2PYsGH417/+pSpa9+3bp3pPv379MGLECBw/fvyxBWZsbCxiY2PV9snlcmRmZjb9IomIiIiIiFoxnc0t8vLyQnZ2tmpbJpNBJBKpTZt1d3eHXC5HUVGRat+VK1fg5+cHAFiyZAl2796Nbdu24d1331UVl1evXsUXX3yhdr7a2lrY2dlp85KIiIiIiIjoETobwYyKisLKlSuRkpKC8PBwbNiwAaGhoXBzc1O1sbGxQXR0NBITE7F8+XJkZWXhu+++w+7du1FYWIi9e/fi8OHD6NSpk9qx27Rpg7Vr18LJyQkvvPACsrKycPjwYfznP//R1eUREWmFUqlUPY6pNTEzM+P0VyIiMggtLRdrO8fqbATT3t4e69evx5o1axAeHo68vDwkJCQAAHx8fJCeng4AWLhwIaqrqxEeHo4lS5ZgxYoV8PDwwOXLl1FbW4uoqCi1Z10uWLAAbm5uWLduHbZu3Yrw8HAkJCTg/fffh4+Pj64uj4hIoxQKBaRSKWpqavQdil7U1NRAKpVCoVDoOxQiImrFampqIJPJ9B2GRmk7x5oolUqlVo5shB7egxkaGsoFCIhIr6RSKWxsbPQdht49rh/4+9r48P8ZERmjqqoqWFtb6zsMrdBWjuX67kREBkapVPIP8P8xMzMDPwclIiJ9UCgULfpxWNrKsS23x4haGf4R3nLI5fIWndCehqmpaYu674WIiIxHSy8wtZVjdbbIDxFpXsHvUnx5rBinc8ogkcrh7W6HiQM74PkQZ32HRkRE1CJU3K/B5pQipGbehVSmQM+uQswe/gyeceVtDET1abklOVELtz/9d0xMPA+JtBYL/tIZG2b4o4+PEEv+m4+P9hdwRJOIiKiZCm9LMSkxCxdEErz1SmesnuoLS3MTTEzMQnpuub7DIzJILDCJjNA3P/+OD/b8hkVjumDVVF9EBjkjrLMA06M7YfOsQBxIL8H272/oO0wi0qGMjAzExMQgODgY48aNw7Vr1+q0kUgkiIuLQ1hYGCIjI7Fr1y7Va4WFhZg8eTJCQkIwYMAArFq1SjV16osvvlBbwd3Hxwd5eXk6uzYifSgV12DmJ5cR/Iw9tv4zEC+EOKOXtxDvT/bBtKiOiP88B3nFlfoOkwiDBg2q8zvax8cHO3bs0Es8nCJLZGQuF0nw4d7fsGSsFwZ3c6nzur9HGyyf2BXzv8xFHx8HeLvb6SFKItIliUSCWbNmIT4+Hi+++CI+//xzvPHGGzhw4IBau4SEBMhkMqSmpqKgoADTp0+Hn58fAgMDER8fDx8fH3z00UcoKSnBa6+9hvbt22PChAkQiURYuXIlXnnlFT1dIZFuKZVKrEi6gnYOVlg63gvmZupjMpOfd8eN0ios3JaHHXOCYWPFhdlIvxISEjBq1Ch9hwGAI5hERkWuUOK9r68ippdrvcXlQ8/5OyK6mwve33ONU2VJqw4ePIgXX3wRQUFBGDp0KI4cOQIAOHbsGAYPHozu3btj1apViIyMxPXr1wEAV69exaRJkxAWFoZRo0bhl19+0ecltAhHjx6Fh4cHRo8eDaFQiJkzZ0IkEiE/P1/VRiaT4eDBg4iPj4eLiwt69OiBwYMH49ChQ6ioqMD58+fx5ptvwsnJCb6+vhg6dChOnjwJACgqKoKnp6eero5I9w6mZuNW9jUsG9elTnH50JyXn4WJCbAl9bqOoyMybBzBJDIiB9JLcLtChplDOz2x7etDO2Hkyl9x8lIZIgKddBAdaZtYWosqmXYeivyQtaUp7G0alxqqqqqwYMECfP755wgNDcX+/fvxzjvvwM/PD/PmzcOmTZsQEBCAtWvX4saNB1O2pVIppk6diri4OHz88cdITU3FjBkzcPz4cdjZcbS9qXJycuDv76/atrS0hKenJ0QiEbp27QoAKCgogEKhgJeXl6qdt7c3zpw5AxsbGyQlJcHJ6cHvCqVSiQsXLsDb2xsAIBKJsG7dOly6dAkODg6YPn06Ro8ercMrJNKNmooK5K7bBKezZzELwI0FB2AbNwsOwUF12lpZmOKtUZ0x+7NsDO3eFl3a2+o+YNILQ8vHhsY4oyZqhWrlSvz7WDEmD3KHwPbJP7ouAkuMfa4dPj5ciP7+jjA1NdFBlKQttXIlXnr3HCqrtPvIDjtrM6Qu7wlzsyd/v5iamiIpKQkBAQEoLS2FhYUF7t27hwMHDiAiIgK9e/cGAMTHx2Pnzp0AgO+//x4dO3ZUTbUcOXIkkpKS8MMPP2DIkCHau7AWTiwWw8HBQW2fnZ0dJBKJWhuBQFCnTWVlJSwtLREaGgoAKC4uxrJly3Djxg0kJiZCoVDA3t4eI0eOxMaNG5GVlYVZs2ahXbt2eO655+qNJykpCUlJSWr7rKysEB8fr4nLJdKKWokEF/9vKe5Um+HrkBnY8FY/3PpmPy4vexd+b8+HY/dudd7Ty1uIAYGO+PhwIVZN9dVD1KRrhpiPAWDhwoVYuHChatvX1xf79+/XVngNYoFJZCS+z7oLsbQWL/dxa/R7JkZ2wFcnb+Hn3HL083PUYnSkbeZmJjiwqJtOPjFtbDIzMTFRFYeurq545plnAAA3btxAhw4dVO2srKxUxU9xcTHOnj0LHx8ftWNFR0dr6ApaJ4FAgKqqKrV9UqkUQqFQtS0UCiGVSh/bRqFQYMuWLfj4448xbNgw/Otf/1L9f9u3b5/qPf369cOIESNw/PjxxxaYsbGxiI2NVdsnl8uRmZnZ9Isk0iKlUokrGz4GzC2wyu4lzB3pC6s2dnhm4niY2dggd/UahH60CtZurnXeOz3aA+M+PI9LhWIEdLLXQ/SkS4aYjwHDugeTBSaRkfgmvQTDe7nCzrrxCwkIbS0wrEdbfHXyJgvMFsDexhz2BvTYtf379+P8+fNISUmBtbU1Ll++jAMHDsDe3h53795VtausrER5+YPl/Nu2bYuIiAh89tlnqtevXr0KF5fH31NMT+bl5YU9e/aotmUyGUQikdq0WXd3d8jlchQVFcHDwwMAcOXKFfj5+QEAlixZgvT0dGzbtg1BQX9MB7x69Sp++OEHvPrqq6p9tbW1cHTk7xRqOUp/TkfZuV9RPHEBrLJqMCj4j+dJu496GfcuXcK1Tz+D3/+9DRMT9T/6n3WzRXT3tvjkcBHWT/f/86GpBTK0fGxouMgPkRG4WVqNs1cqENOz7VO/N/a5dkjPq8Bvv9/XQmTUmtXU1EChUEAqlaK4uBhr1qwBAPTo0QNHjhzBr7/+ColEgg8++AA1NTUAgIiICFy+fBk//PADpFIp0tLSMGbMmDoja/R0oqKikJubi5SUFIjFYqxevRqhoaFwc/tjxoONjQ2io6ORmJgIsViMn376Cd999x1iYmJQWFiIvXv3YsuWLWrFJQC0adMGa9euxf79+1FZWYnTp0/j8OHDGDZsmK4vk0grFDU1KPhyOzqMHIH/XqxFbP/2aiNHJiYm6PzaNJRnXUDZL+fqPca0qI7IyK/ApUKxrsImMlgsMImMwJFf78DH3Q5e7Z9+EZRn3WzRq6sQX/94SwuRUWv28ssvw93dHQMHDsSMGTNUz1DcuXMn3nzzTcyePRsREREQCoUQCAQwNzeHk5MT1q9fjzVr1qB3795Ys2YNEhMT0a5dO31fjlGzt7dX9Wt4eDjy8vKQkJAAAPDx8UF6ejqAB/foVFdXIzw8HEuWLMGKFSvg4eGBy5cvo7a2FlFRUWrPUFuwYAHc3Nywbt06bN26FeHh4UhISMD7779fZ5ozkbG6c/In1N6/j9vBkSipkOGl3nWnwVq3a4f2Q6NR9NWueldn7+hijchgZ/z3xE1dhExk0EyUfIaBysP7Q0JDQ2FmxucZkeGYuu4CnvN3xKsvdGzS+09cLMXSnVfw7eLufFaXEaitrQUAmJsb510MFRUVEIvF6Njxwffr/fv30bNnT5w9exY2Nk83p+hxfcHf18aH/8/IECmVSmTOngPnfn2xzaQ7KqvlSPird71tq++W4pfpr8N/0UI4hIbUef2iSIxp6y9i79th6OBkre3QSQeMPR8/ibZyLEcwiQzcnXsyXCqUICKg6Y8aCfdzgLWFKY5m3X1yY6Jmys/Px4gRI5CXl4eysjKsXr0aYWFhT11cEhFpW3nmeVTdvAWnF6JwLOsuoht4xrSVsxPcno9E8b76V+YMfMYeQZ72+OokZwxR68YCk8jA/ZRdhnaOVujcrul/nJubmSKmZ1scOFOiwciI6tejRw9MmzYNU6dOxYABA3D16lXVdE0iIkPy+5E0uPQPR8ZNBUxNgH6+Dg22bzd0CMrPZ0F6s/4icsKADjiQ/jskVbXaCJfIKLDAJDJwJy+VISLAsc6qdU9reC9XZF4TQ1TCxVRI+/7xj3/gxx9/RFZWFr788kvVqqVERIai5p4YpWcy4PrCIJy8VIb+AU6wMG/4T2O7ZzpB4OeL34+k1vv6cwGOENiaI+XcHW2ETGQUWGASGTC5Qolfrt5DH5+GP1FtjE5tbdCtiwD70zmKSUREdPuHk7BycUEbX1+cyi5HuF/jcq3b4CiUHD0GRW3dUUozUxOM6O2Gfad/r3cxIKLWgAUmkQHLK66EtFqOkGc18+DmEb1d8e3Z26ip1e7Dgal5zMzMIJfL9R2GQZDL5VwQhoi04vaJH9A2cgByiitRcb+m0R/mOvfpDXm1DBVZF+p9/aVerrh26z4uFUo0GS6R0WiZSyIRtRDnrt6Db0c7tLHWzI9qZLATPtz7G368XIbIRx4iTYbFxMQEpqamqKqqgqmpKUxNW99ngQqFAgqFAmZmZs2eHk5E9GfVd+5CkpePrnGzkHKpHCHPCmBv07hca2ZtDefevXD7xEk4dgur83pboSUiApyw9/TvCHxGMx8QExmT1vdXC5ER+eXqPXTrItTY8awtzBDdzYXTZI2AhYUFrK2tW+zS6E9ibm4Oa2trWFhY6DsUImqB7p7+GTYdO8LWoyN+zitH3ycs7vNnLhH9cffndMirq+t9fWRfN6Rm3oVEysV+qPVpnX+5EBkBuUKJzGv3MKqvm0aPO7yXK15dewElFdVwFVpp9Nikea11BJOISJvunv4Zzv36QFotR3ZRJeaM8Hyq9zuEhsDUwgKlZ86i7XPhdV7v2VUIoa05jmWV4qXerhqKmsg48K8WIgN17dZ9SKo0d//lQ74d7dC5nS0On+UKd0RE1PrIystx73I2nPv2wQWRGJbmJvDtaPdUxzA1N4dzvz64e/rnel83MzXB4G4uOPzLbU2ETNSg69evw8fHB6+88kqd1/bt2wcfHx989NFHOouHBSaRgbpUKIGnq02j7wlpLBMTEwzv5YrkjBKucEdERK1O2dlzsGrbFnbPeuLXa/cQ7GkPc7On/5PYuXcvlP1yDoqamnpfH9q9LX65eg+3yuqfRkukaSKRCEVFRWr7vv32W9jb6/ZeYBaYRAbqcpEE/p3aaOXY0d1cUHy3GlkFYq0cn4iIyFCVnTsHx+5hMDExwa/XxAjtLGjScYTBQQDw2NVku7S3hXcHWz4Tk3Tm+eefx7fffqvaLi8vx6+//op+/frpNA4WmEQG6lKhBAEe2ikwHdtYICLAEclnOHWHiIhaD6VcjvLMLDh2C4OsVoGLIjHCmlhgmlpYwLF7N9xNP/PYNkN6tMXhX25zxlALUyUTQyy9o9WvKtnTDwIMHToUhw4dUm2npqYiPDwctra2mrz8J+IiP0QGSFotx7Vb9xGgpRFMAIjp5Yr/25GHuS97wsaKzxkkIqKWT5yXD0V1NYRBgci9cR9yhRL+HnXvv6yprYKo5FdIqu7Cyd4DHZ0DYGpa989m5z698NvWL6Gc8RpM6lmQbXCYC9Yni5BzvRJ+WvrQmHRLoajF+oN/QXVNpVbPY2Vhh7kvH6r3++5x+vbti9u3b+Pq1avo0qULDh8+jDFjxuD48ePaC7QeLDCJDFBucSVMTUzg1UF7nzj19XGAjaUZjmbdRUxPrnBHREQtX9kv5yDw94OZjQ0uF91C53a2sLZU/5D1ys3TOJTxAWS1Ughs3VAqLoKDXXtEd5+DZ916qLV17N4N+Ws3QJyXD4GvT53zuQgs0cNLiLTzd1lgthCmpub4Z8xu1Mi1e2+thZnVUxWXwIOV5wcPHozDhw9j/PjxyMrKwqZNm1hgEhFwqUgCb3c7WJprbxa7uZkJhvZoi+QzJSwwiYioVSg7l6l6rMjlIgn8OqoXfTnXT2Dv6SV4zn8K+vlOgJmZBapkEpzK2YGdJ+YiKvSf6On9F1V7czs7CAL8UZZxtt4CEwCeD3HGv48VY9awTjAxMdHexZHOWFvawxq6XTinsYYOHYqlS5fC2dkZERERsLa21nkMvAeTyABdLpTAXwefdA7v6Ypfr4lx/U6V1s9FRESkT7LyclRevQrH7mEA6i6md/deIfanv4uo0H/iuYApMDOzAABYW7bBoOAZ+Ev4e0g7vxG/XPlG7biO3buh7JdfH3veAYFOuFVWjdxi7U6pJAKAHj16QCwW47PPPsOQIUP0EgMLTCIDlHO9En5P+UyupvB0s0HQM21wMKNE6+ciIiLSp/JfM2Hp7AwbDw/cr5aj4Hep6v5LpVKJ5IwEdG3fFz28RtX7fm/3/ni5z2J8d+4jFN4+r9rv2L0bKn/7DdV3S+t9n5O9Bbp1EeBYVv2vE2mSqakpoqOjUV5ejoiICP3EoJezEtFj3a+W4/rdKni7a7/ABIDhvVxxMOM25AqucEdERC1X+fkLcAgLgYmJCXKuV8LczARe7R+sdXC56BhKyq8gKiyuwWmsfh6R6O0Ti32nl6pW+bRx7wArN1eU//r4UcxBIc44ev4uV5MlrejYsSNyc3Nhbv7g7sd33nkH586dg5WVFQDg/fffx5tvvqmzeFhgEhmYKzfvw9TEBJ5uNjo53wuhzqi4X4uM/AqdnI+IiEjXlEolKi5chDAoEACQV1yJLu1tYW5mCqVSiR8vfYk+PuNgb+PyxGMNDHoNtlYOOJb1KQDAxMTkidNkI4OccP1uFa7cvK+ZCyIyYCwwiQxM/o1KPOtmo9UFfh7Vxtocg4KdkHyG02SJiKhlqrr1O2R37qgKzPybleja/sFMoas3f0Z55U306PpKo45lZmqOoT3m4ddrybh+5yKAB9NkyzPPQ1FbW+97nO0tEdZZgKPn72rgaogMGwtMIgOTf+M+umrx8ST1eamXK05cLEXF/RqdnpeIiEgXKi5chHWH9rBydgYAXL15XzU99ufcrxDy7DDYWgkbfTx3Z3+EPjsUR89vglKphDAoEIqaGohzcx/7nsggJ5y4yPswqeXTaYGZkZGBmJgYBAcHY9y4cbh27VqdNhKJBHFxcQgLC0NkZCR27dqleq2wsBCTJ09GSEgIBgwYgFWrVkEulwMAfv/9d0yZMgUhISEYPHgwjh07prPrItKkvBuV8O6gm/svHwrrLICLwBJHzvGTVSIiankenR4rVyhx9ZYUXh1sUSq+joKSX9CzkaOXj3ouYCpuluXg6s2fYWZlBWFgAMrOnmugvSOu3pKi+C5XbqeWTWcFpkQiwaxZszB58mScPHkSvXr1whtvvFGnXUJCAmQyGVJTU/Hhhx/i/fffx8WLD6YfxMfHo1OnTvj+++/x6aef4sCBA/jqq68AAPPnz0enTp1w/PhxxMfHY+7cufj99991dXlEGqFQKHH1pu5HME1NTTC8lyuSuZosERG1MKr7LwMfFJjFd6tQXaOAVztbZBWkoKNzEJwFnZ76uALbtujZ9S84lvUJlEoFHLuHoezc4+/D7OBkDa/2tjh5qazJ10JkDHRWYB49ehQeHh4YPXo0hEIhZs6cCZFIhPz8fFUbmUyGgwcPIj4+Hi4uLujRowcGDx6MQ4cOoaKiAufPn8ebb74JJycn+Pr6YujQoTh58iRu3bqFs2fPYt68eXB0dERUVBSCg4ORlpamq8sj0ojrd6sglSnQVccjmAAwrEdb5BZXIv8Gn9NFREQtR9WNm6gpK4MwMADAg+mxLgILCO3MkFVwGMHPNv1ZgX19J6Cs8gbyb5yCY/duuF8gQvWdx88Gei7AET9c4jRZatl0VmDm5OTA399ftW1paQlPT0+IRCLVvoKCAigUCnh5ean2eXt7QyQSwcbGBklJSXBycgLw4NOoCxcuoH379sjOzkbHjh1hb2+v9r6CggLtXxiRBuXfuI+2Ags4trHQ+bnbOVqhp5cQyRm3dX5uIiIibam4cBE2Hd1h6eQIAMi/eR9d2tmi6M4FVFaVwt9jUJOPbWslRLfOL+FU9g5Yt28P63btGhzFjAhwxK/XxBBL618MiKgl0FmBKRaLIRAI1PbZ2dlBIpE8sU1lZSUsLS0RGhoKACguLsb06dNx48YNzJgxo8H3PU5SUhJGjRql9jVx4sTmXiZRs+TdqNTL6OVDw3u5IuWX26ipVegtBiIiIk169P5LAKpbUfJv/ARP126wtmzTrOP38hmDG6XZKLqTBcfuYSg/9/j7MP06toGDnTlO55Q365xEhkxnBaZAIEBVlfpNzVKpFELhHyt2CYVCSKXSx7ZRKBTYvHkzYmJi4Orqin379sHNze2J76tPbGws9u7dq/a1Y8eO5l4mUbPoYwXZRw0IckStXIkfL/P+ECIiMn5KpRIVFy+pFZhXbt6HV3s75N84ha4d+jX7HEJbNwQ+Mxinsv8Dh25hKD9/4bGPKzE1NeE0WdKaXbt24eWXX0ZwcDC6d++OSZMm4fjx4zqPQ2cFppeXF7Kzs1XbMpkMIpFIbdqsu7s75HI5ioqKVPuuXLkCPz8/AMCSJUuwe/dubNu2De+++y4cHBwAAJ07d0ZBQYFaAfvo+4iMRb6eRzCtLcwwuJsLDvCZmERE1AJIi66jprxcdf9lVY0c1+9WwU1Yjjv3CuDVIVwj5+nnNx5Xbp6GzMP2weNKch7/uJLnApxwKrsctXLOFiLN2bBhA1avXo1XX30VJ06cwKFDhzBgwAD885//xKFDh3Qai84KzKioKOTm5nnk+1cAACAASURBVCIlJQVisRirV69GaGgo3NzcVG1sbGwQHR2NxMREiMVi/PTTT/juu+8QExODwsJC7N27F1u2bEFQUJDasT08PBASEoI1a9agsrISBw8eRHZ2NiIjI3V1eUTNVnG/Br+Xy/Q6ggkAQ3u0xc+5FXwmJhERGb2Ki5dg+0wnWPxvVlvR7SoolUBVzTm4CrvAwa6dRs7jIvBE1w79cKZg34PHlfzy+GmyPbsKUCNX4tdrYo2cm+j27dv47LPP8MEHH2DEiBFwdHREu3btMG3aNEyfPl3t0Y66oLMC097eHuvXr8eaNWsQHh6OvLw8JCQkAAB8fHyQnp4OAFi4cCGqq6sRHh6OJUuWYMWKFfDw8MDly5dRW1uLqKgo+Pj4qL4WLFgAAPjggw9w6dIl9O3bF59++inWrVuntugPkaG7evM+rMxN4OFio9c4AjzawNneAj9e5v0hRERk3B48niRAtV14uwquQkv8VqKZ6bGP6u0di0uFqbAN8mlwoR9rCzP09hbiJKfJGq1aSSWq75Zq9atW0vhV/U+fPg17e3sMGDCgzmsjRozAjRs3cOPGDU12QYPMdXYmAH379kVKSkqd/bm5f0wjcHBwwKZNm+q0iY6OVmv3Z+7u7ti+fbtmAiXSg/wb9/FsO1uYm5noNQ5TUxMMDHLC91l3MaxHW73GQkRE1FRKhQIVFy/B6/Xpqn2i21J4ugKFJZkYGPh3jZ7vGdcwuAieQaHVTZgXiFB99y6snJ3rbRvu54gdx29gzssaDYF0QCmX4+zfZ0B+/75Wz2Nma4veO76EiZnZE9vevHkT7du3r/e1tm0f/C13584deHh4aDTGx9FpgUlEj3f11n14tdfv9NiHIoOcMPuzbNyvlsPW6sm/2IiIiAzN/cIi1IrFEASoj2C6O+fDysIeHZw0u1aHiYkJennH4mjmRkS064Tyc7/CLeqFetv283VAwu5ruH6nCh1drDUaB2mXiZkZenz2CeTV1Vo9j5mVVaOKS+DBQql379b//NXr168DAFxdXTUW2/+zd+fhUZfn4v/fM9m3mez7RshCWLIRQkIAZRMEFK2l1O2gXU491XroEb/q6e8n36NtseXUWu2mbbUurSLuoqAsoqyBhDWQfZns+zrJZJnl+0dKJAaSAZLMTHK/rivXBZ95Ph/u8RKeued57vsZzYRtkRVCjKy4xnoSzKQoFW7OdhzOk26yQtiKEydOsHbtWhISErjzzjspLS0dNkar1fLwww+TnJzMkiVL2LFjx+BrFRUVbNy4kcTERG644YYhNTv19fXcd999JCYmsnLlSvbv3z9h70uIa9V+Lhe3yAgcVF+XTFU06HB1Okd0UAZK5dh/gTorfBkKpRJDtPeIdZgBXk5EBbpwJF/mWVtk7+6Gk4/3uP7Yu5vf9DEzM5Pa2lpOnRq+Nfv9999nxowZhISEjOV/ghFJgimEFTAaTf9awbRcB9lL2SkVLJ7lzYFzUh8ihC3QarU89NBDbNy4kYMHD5KWlsamTZuGjdu6dSt9fX3s2bOHbdu28cwzz5CbmwvA5s2bCQ8P54svvuDFF1/ko48+4q233gLgscceIzw8nAMHDrB582YeeeQR6uvrJ/Q9CnG12nPPo7qk/tJkMqFp7KKn7xQxY9Q99pvs7RxJjf4WxR6lIx5XAgOrmHIephgLYWFh3Hffffznf/4ne/fupbW1lc7OTrZv38727dvZsmXLhMYjCaYQVqCmpRddn9FqVjBhYJvskbw2+vXSRl0Ia7dv3z7CwsJYv349arWaBx98EI1GQ1FR0eCYvr4+du7cyebNm/H19SU1NZWVK1fyySef0N7ezpkzZ/jpT3+Kt7c3M2bMYPXq1Rw8eJC6ujqys7N59NFH8fLyYsWKFSQkJLB3714LvmMhRmYyGuk4P/T8y7YuPfZ2ZRiM3UQFzhu3Pztl+jqq1HUY+npHPK4kY4YX2cUd9PbLPCuu32OPPcYDDzzA7373OxYvXszSpUvZv38/r776KikpKRMai9RgCmEFimq78PZwwNvDwdKhDEqJVqE3mjhT3klqtNrS4QghRpCfnz/kXGlHR0ciIyPRaDTExMQAUF5ejtFoJDo6enBcbGwsx48fx8XFhe3bt+Pt7Q0MrPScO3eO2NhY8vLyCA0NHdKZPTY2lvLy8ol5c0Jcg25NBXptF+pZX/+9qGjUEeyVR7h/Mk4O47djyM3Zi9nRK9GFnqM15+SQLraXSprmgVIBJ0s6yJjhOW7xiKnjrrvu4q677rJ0GLKCKYQ1KKntJsaKVi9hoI16arSKI1KHKYTV6+zsRKVSDbnm5uaGVqsddUxXVxeOjo4kJSUBUF1dzY9+9CNqamp44IEHRrzvSrZv3863vvWtIT/33HPP9b5NIczWnpuL27RI7N3dB69pGnoI8ckjdoyPJ7mctNjvUOXdQNOJrCuOcbBXMi9GzVGpwxSTjCSYQliB4tpupltZggkDbdQP50l9iBDWTqVS0dPTM+SaTqdDrf5694FarUan011xjNFo5KWXXmLt2rX4+/vz/vvvExAQMOp9l7Nhwwbee++9IT9vvPHG9b5NIczWfu78sJXDsvoKnB1rx/z8y8vxU0/DNTGO3spaeq/Q3RMgY4YnR6QOU0wykmAKYQWKrKiD7KUyZnhSVq+jpqVn9MFCCIuJjo4mLy9v8Pd9fX1oNJoh22ZDQkIwGAxUVlYOXisuLiY+fuCohi1btvDOO+/w2muv8fOf/xxPz4Ete1FRUZSXlw9JYC+9TwhrM1B/eQHV7NlDrje0H0epCMHLfWK6aaak3Y3O3UDD8SNXHLNghhcVjT1UN8s8KyYPSTCFsLCePgNVzT3EWEkH2UuF+DgT6e8i364KYeVWrFhBQUEBu3fvprOzk9/85jckJSUREBAwOMbFxYVVq1bx7LPP0tnZyeHDh/nss89Yu3YtFRUVvPfee/z1r39lzpw5Q54dFhZGYmIizz33HF1dXezcuZO8vDyWLFky0W9TCLN0lWvQdw2tvwTQG07i45E2YXFMD5pPd4QjmkOfXXFMkLcT0wJcOCK7hcQkIgmmEBZWWqdDqYDIABdLh3JZC+I9ZeITwsp5eHjwwgsv8Nxzz5GZmUlhYSFbt24FIC4ujqysgTqwJ554gt7eXjIzM9myZQtPP/00YWFhXLhwAb1ez4oVK4iLixv8efzxxwH41a9+xfnz58nIyODFF1/k+eefH9L0Rwhr0pF7HreoaUPOEezu7cLVqYiYkPHfHnuRQqEgKONGDAU19PfqrjhuYJus1GFaI6VSidE4ebv8Go1G7OzG/jxY6SIrhIUV1XYR7ueCk4N1ft+zYIYn7x0toF9vxMHeOmMUQkBGRga7d+8edr2g4OtjEjw9PfnjH/84bMyqVauGjPumkJAQXn/99bEJVIhx1n4ud1j95amSIxiMTiROS57QWOYsvYcTr+zl3MG3SFl+/2XHLJjhybtH6untN1rtZ4GparInmAaDAUdHxzF/riSYQlhYSa111l9elDDNA6PRRG6FluQo1eg3CCGEEBZiMhhoP3+BgBXLhlzPrzpMS2c83u5OExqPs5sKpvlReWgPycvuQ6FQDBuTFKVCoYDTpR3Mj5PjSqyNnZ0dvb2947LSZylGoxGDwYCT0/j8fZCvSYSwsCIr7SB7kbODHXMiPThR1G7pUIQQQogRdWk0GHQ6VJc0uDKZjDS0n0ChSL5sgjfeQjOX4VLaRUXj6cu+7mivJCVKRVahzLPWyMHBYVxW+SzJwcEBFxcXlMrxSQUlwRTCgkwmE8U11ncG5jfNi1FLgimEEMLqtZ87j9u0ofWX1c0XMBi1+KhSLBJTUOaNuLXbk5115aN60mJlnrVmCoUCe3v7SfMz3l+0SIIphAU1dvTR3q0n2go7yF5qXoyaXI2W7l6DpUMRQgghrqj9XC7qOUPrLwurD9LTH0uEn7dFYnIO8McxPAhtzjlaOqsuOyYtVk1BdRet2v4Jjk6IsScJphAWVFDVhcrVnkAv6956ER/qjrOjktOlHZYORQghhLgsk8FAx4ULwxr8FFQfpLZlFuF+luvWHpC5mJB6H44X7rjs69MDXfH2cCBbVjHFJCAJphAWlF/VRXyom0VqQq6GvZ2ClCiVbN8RQghhtbrKNRh0Pahmxg9ea+6ooLmzgqLaOCL8nS0Wm8/8NFxq+7iQt4uevs5hrysUCtJi1FKHKSYFSTCFsKD8qi7iQqx7e+xFqVKHKYQQwoq1n8sdqL90+3peLag+iJd7DL39noT6Wi7BdI2MwMnPj+B6T06VfnzZMWmxao4XtWEymSY4OiHGliSYQlhQQXUXM8LcLR2GWebFqCis6aZN6kOEEEJYobYzZ/FMnDPkWmH1IdycUgn0dMLZwXLHTCgUCrznzyeiMYDs4vcxmYafrZgWq6autY/Kph4LRCjE2JEEUwgLae7so6G9jxk2soI5PdAVL3d7TkkdphBCCCtj7O+nI/c8nkmJg9c6dU1UNefS059MuJ/lVi8v8klPw1TcQHdHM5rLHFnir3ZiWoALx2W3kLBxkmAKYSEFVV24O9sR4jOxhz5fK4VCQdI0lSSYQgghrE5HXj4AqvgZg9cuVOzDVxVBTYuvRRv8XKSKn4GdswuzexM4W7b7smPmxag5LnWYwsZJgimEheRVdRFnAw1+LpUcpeJU6fDmBEIIIYQltZ85i2pmPErHr7uyn6/Yy6zw5Wgae6xiBVNhZ4f3vFQCq93Jq/qCvv7uYWPmx6rJLmrHYJQ6TGG7JMEUwkLyq7Q2sz32ouQoFYU1XWh1ekuHIoQQQgxqO31myPbYls4qalrymBW+nIrGHiL8Lb+CCeC7cAF958pwxpX8qi+HvZ48XYWuz0BepdYC0QkxNiTBFMICTCYTuRotsyI8LB3KVYkOdsXVyY4z5bKKKYQQwjr0d3SiLSkdkmCer9hLsHc8RpM/XT0Gq1jBBFAnJqB0sGdObyJnNcO3ybo72zM73EPqMIVNkwRTCAuobe2lubOfORG20UH2IjulgqRpHpwqkTpMIYQQ1qH93DkcVCpcI8IBMJmMnCn7hDmRq6ho7MHRXkGgp3X0O1Da2+OTno53qQJNwym6elqHjZkXK3WYwrZJgimEBZwt7yTA05EAK5nwrkZSlIrTZbKCKYQQwjq0nTmLOjEBhXLgY21p3Qm6elqZE7GSikYdYb7OKJXW0+/Ad+ECdGcK8bT3o7D60LDX02LUnC3vpKfPYIHohLh+kmAKYQHnyrXMsbHtsRclR6m4UKmViU8IIYRVGKi/TBj8fU7JB8wKX46zozsVjTqrqb+8SD1nNnYuzsRr48mvOjDs9Vnh7tjbKTgr5SjCRkmCKYQFnNN0khBpmwlmfKgbdkoFuRXSgEAIIYRl6Wrr6K1vGKy/7OhuoKjmMCnRtwGgaeixiiNKLqWws8NnQQaepSbKGnLQ9Q0tO3GwV5IY6UF2sZSjCNskCaYQE6y710BRTRdzIm2r/vIiB3slcyLcpQ5TCCGExbWdOo1LaChOPj4AHC98myCvOIK9B87DrGjUWU2Dn0v5LsykJ7cED4UnRdWHh72eGq0mp1jqMIVtkgRTiAl2urQDZwc74kJsM8GEi+dhSoIphBDCslqzc/BKTQGgu7ednJIPWThzIwB6g5Gq5l6rW8EEUMXPwN7dnfiOGZc9rmRu9EA5SlePlKMI2yMJphAT7ERROynTVdjbWU/DgauVPF3FOY2Wfr3R0qEIIYSYogy9vbSfy8Vr7kCCebxwB15uwcQELwCgurkXg9FEhL/1rWAq7OzwW7wQdWE/ZfXZ6A29Q16fEeqOs4MdZ8rky1xheyTBFGKCHS9qZ16M2tJhXJfZ4e4YjCbyqrosHYoQQogpqv1cLgp7e1Qz4+nubeNE0TtkzrwXhWLg462mQYe3uwNqVwcLR3p5/ktupC9fg6NOiabxzJDX7O0UJEVJHaawTZJgCjGBWrX9FNV023yC6exox8wwqcMUQghhOa3ZOXgmJaK0t+er3Jfxdg9hZtjSwdfLG3SEW+Hq5UVu0yJxDQ8jrmEaJTVHh72eGq0mp0TqMIXtkQRTiAmUXdSOt4cDUYHWVw9ytZKmeXBatu4IIYSwAJPJNFh/2dRRzsmSD1mR/PDg6iWAplFHpJUdUfJN/ktuxLNQT3HNkWGvzY1WUVDVhVant0BkQlw7STCFmEBf5rawMN4LhcJ26y8vSp6u4nRZJwajydKhCCGEmGK6KyrpbWzCMyWZPad/T2zIIsL9EoeM0TT0EGGFDX4u5bt4Eab6dvoq62nprBzyWkywG+7OdtJUT9icCU0wT5w4wdq1a0lISODOO++ktLR02BitVsvDDz9McnIyS5YsYceOHcPGvPvuu2zevHnItVdeeYW4uLghP4WFheP2XoS4Wn16I4fz2rhxjrelQxkTCZEedPcaKKnttnQoQgghppjW7BzcY6Ip7zqHpuEkyxL/Y9gYTYOOCCtfwXTy8cYzMYGomkCKa48Nec1OqSB5uooTUocpbMyEJZharZaHHnqIjRs3cvDgQdLS0ti0adOwcVu3bqWvr489e/awbds2nnnmGXJzcwEoKCjgpZdeYtu2bcPu02g0/PKXv6SgoGDwJzY2dtzflxDmyi5qx4TJ5usvL/JwsScmyFW+WRVCCDHhWrNzUCXP4bNTz5EZ/294uYcMeb1N2097t94qO8h+k9+SG/EuUVBcNXybrJyHKWzRhCWY+/btIywsjPXr16NWq3nwwQfRaDQUFRUNjunr62Pnzp1s3rwZX19fUlNTWblyJZ988gkAhYWFaDQagoKChj2/srKSyMjIiXo7Qly1A7ktLJjhhZPD5NmZniTnYQohhJhgeq2WjvwCyryqsLdzImPGXcPGaBp1ONgpCPKy/gTTJz0NpR46z5ynX98z5LW50SqKarpp6+q3UHRCXL0J+6Sbn5/PzJkzB3/v6OhIZGQkGo1m8Fp5eTlGo5Ho6OjBa7GxsYNjbrnlFn7xi1+wdOnXHcIu0mg0PP/886SmprJ8+fLLbq0VwlJ6+g3sPd3MiiQfS4cyppKjBuowTSapwxRCCDExWrJPYufhxrHOXdw89xHs7RyHjSlv0BHq62wTZ07bOTvjsyCDwDJnKprODnktKsAVL3d76doubIr9RP1BnZ2deHp6Drnm5uaGVqsdMkalUg0b09U18ll7RqMRDw8Pbr/9dv7whz9w9uxZHnroIQIDA1m0aNFl79m+fTvbt28fcs3JyWlYbacQY+HAuRYc7BUsnOll6VDGVFKUBy2d/VQ0WX8jBSGEEJND89FjtITriQ9fwrSA1MuO0TT0WH0H2UsFLF1C41dfUV5+lOmBaYPXlUoFKdPVZBd3sCRhcn1JLSavCUswVSoVPT1Dl/11Oh1q9df1aGq1Gp1ON+KYy1Eqlbz//vuDv1+wYAHr1q3jwIEDV0wwN2zYwIYNG4ZcMxgMnD592qz3I8TV+CirgdVz/XCwnzzbYwF8PBwJ83XmdGmHJJhCCCHGnaG3l5aTOVTd0MldST+54jhNg47pQa4TGNn1Uc+ehcLDhdajJyB96Gtzo1XsOFRnmcCEuAYT9mk3OjqavLy8wd/39fWh0WiGbJsNCQnBYDBQWfl1m+bi4mLi4+NHfHZJSQmvvPLKkGt6vR43N7cxil6Ia1deryOnpINb0vwtHcq4SI5Scaq009JhCCGEmAKas0+gp585S+/B3eXKK3qaRuvvIHsphVKJ16J0XHPb6e5tG/Ja6nQ1ZfU6mjv7LBSdEFdnwhLMFStWUFBQwO7du+ns7OQ3v/kNSUlJBAQEDI5xcXFh1apVPPvss3R2dnL48GE+++wz1q5dO+Kz3d3d+d3vfseHH35IV1cXR48eZdeuXaxZs2a835YQo3rjQA0ZcZ5EBdrON6lXIynKg9PS6EcIIcQEKNyzg/YIBfPivn3FMX16I9XNPUTaQAfZS0WuWoe6yZGS8/uHXI/wd8ZX5cBJOa5E2IgJSzA9PDx44YUXeO6558jMzKSwsJCtW7cCEBcXR1ZWFgBPPPEEvb29ZGZmsmXLFp5++mnCwsJGfHZAQADPP/88f/vb38jMzGTr1q0888wzxMXFjfv7EmIkTR197Mpp5J4lwZYOZdwkR6moaemlvq3X0qEIMaXJWdNistNqGzGcryTshlWXbexzUXmDDoNxoEGOLXENDaU/2JWGfQeGXFcoFMydriZbGv0IG2F2DWZ5efl1HwOSkZHB7t27h10vKCgY/LWnpyd//OMfR3zOT34yfM/94sWLWbx48XXFJ8RYe2VvNbEhbsydrhp9sI0K9nbCX+3I6dJOVqY4WTocIWzS9c6xF8+a3rx5MzfddBMvv/wymzZt4qOPPhoy7tKzpsvLy/nRj35EfHw8s2fPpqCggC+//JKXX36ZhQsXDrnv4lnTd9xxxzXHKMT1OrrrBZyNCuasuHfEcaW13YT4OOHiZDdBkY0dtwWJaHcfxWQ0olB+vQ40N1rFGwdqLBiZEOYzewVz1apV3HHHHbzyyivU1UmhsRCjqWrq4b2j9Ty4JhyFwvrbpF8rhUIxsE22TL5ZFeJaXe8cK2dNi8muvbue1uM5uM6Jw9555K2vxbXdTLfRspSom27HvttEdfbBIddTo9VUNPbQ0C67hYT1MzvBfOONN0hNTeX1119n6dKl3H333bz55pu0t7ePZ3xC2Kw/7apgfqya1OiRuyBPBklRKk5JHaYQ1+x651g5a1pMdkcvvEFAtSthN64cdWxJXbdNdZC9lE/AdDoilFTt+XTI9RAfJwK9HMmROkxhA8xOMFNTU3niiSfYv38/b7/9NvPmzeONN95g4cKFPPzww3z11VfjGacQNuVChZZ9Z5p5cE24pUOZEMlRKkrrdLR19Vs6FCFs0vXOsVc6R3qsz5o+cOAATz31FFu3buXgwYNXvGf79u1861vfGvJzzz33jPjnCHElHd2NlGTtxr4HvFMvf+7lpUrquom20RVMAMe0OPpPl2K45Hg/hUJBarSa7CJZ2BHW75rOwYyLi6O5uZnGxkZqamo4evQoWVlZeHp68vTTT5OWljb6Q4SYpEwmE7//RMOquX7EBE+No3Km+bugcrXnTFknN8z2tnQ4Qti0a5lj5axpMZkdy/8nEXX+eM2djb37yPOqtkdPXWsfUTa6ggkQsmApDR/m0Xz0GP5Lbhy8nhqt5s+7KzCZTJO69EbYPrNXMLu6uvj000/56U9/Snp6Ops2bUKn07Ft2zYOHz7MwYMHufnmm3n88cfHM14hrN6R/DbOlnfyo5Ujdz+eTJRKBUnT5LgSIa7V9c6xcta0mKy6elo5WfwhPmVK/BYtHHV8aa0OezsFEX62dUTJpaYFzaMuQkfVnl1DrqfGqKhr7aO6WeowhXUzewUzPT0dhULBggUL+J//+R+WLVuGi8vQA2xXr1592S6xQkwVeoOJ5z/WsGFREEHeU6ujanKUij2nmywdhhA26Xrn2BUrVvDLX/6S3bt3k5mZye9///sRz5p+6qmnOHv2LJ999hnvvPPOiLFdPGva29ub5cuXc/bsWXbt2sU//vGP63/jQowip/h9wjqCoFeP9zzztsdG+rtgbzdhJ/GNORcnFf2J/uh2FNPb2ISTny8A/monIvycyS5uJ9TXdhNoMfmZ/bfvySef5NChQ/z5z39m7dq1wyY+GGgWIAmmmMp2nmigpbOf+5aFWDqUCZcU5UFBdRfdvQZLhyKEzbneOVbOmhaTkd7QS07x+0Q3huM1by52l/l78U3Ftd1EBY4+ztoFzUqjz9eRxi+H1l+nxkgdprB+Zq9g/vnPf2bRokXDGgRUVlby4x//mI8//njMgxPClnT3GnhxdyU/uCkUD5drKm+2aXEhbjjaK8nVdJIW62npcISwKWMxx8pZ02KyydXsQWFUQG4Nfg+tM+uewuouFs3yGufIxl9kQArHInah3v8FIXfcPlhzmRqtZtt7ZVKHKazaqJ+CH3roIQCqq6v52c9+Nuxb1draWhobG8cnOiFsyBsHanB1suNbGQGjD56E7O2UzInw4FRphySYQphJ5lghLs9kMpFV+DYpLMCoP4dnSvKo9xiMJgpquvjhJOiBEOabyPvhWqadrENbVIxHbAwAc6NVtGj7Ka3T2exRLGLyGzXB9PDwGPy1m5vbsKJ+b29vNm/ePPaRCWFDWjr7+ceBGrbcGY2Dve3WfVyv5Okq2bojxFWQOVaIyyurz6alswpfTQL26WnYOY3e10DToKOnz8iMUNtvQOXk4IpfSDzEKGjYf2AwwfR0cyA22JXs4nZJMIXVGjXBvFjDAfDoo4/i7S1HEAjxTW8cqCHcz4Ulc6b234+kaR78fW8VfXojjlM40RbCXDLHCnF5WYXbmROygo53TxO7+adm3ZNf1UWIjxMq18lRphIZMJeG6cexP3iIad+/D6WDAzBQh3miqJ0Ni4IsG6AQVzDi38B9+/aRmZmJs7Mzy5cv59SpU1ccu2zZsjEPTghb0NLZzztH6vj53TFTvh5iVoQ7BhPkV3aRMM1j9BuEmMJkjhXi8po6yimpzeIun8002uXjmZhg1n35VVpmhLqPc3QTJzIghSzPfxJkDKU1OwefjHRgoA7zw6wG9AYT9nZT+3OHsE4jJpgPPvgge/bsISwsjAcffPCK4xQKxZDzt4SYSt44UE2kv8ukaCpwvZwd7JgZ5s6p0g5JMIUYhcyxQlxeVsHbRAXOo+9YEd7p8wdX7kaTV9XFwpmTZy4O9ZmFyQ6c58bRsP/AYIKZFOVBT5+BwuouZoZPnoRaTB4jJpj5+fmX/bUQYkBLZz87Dtfzy3tl9fKi5CgPTpd1sJGpd1SLEFdD5lghhuvubeOcZjd3JP9fGo//gVn/9/836z69wURhdRc/vMn2G/xcZG/nRKjvHNpdHHH42wn629txUKtxd7ZnZpg7J4rbJcEUVumqbpLr9gAAIABJREFUiqQaGxtpaho4SP3cuXO88MILfPXVV6PcJcTk9e6ROsJ8nSfVN6bXKylKxZmyTgxGk6VDEcKmyBwrBOQUf4CXWwjuRT04enmimjXTrPuKarro7Tcya5IlXNP8UylzKsHJ34+mQ0cGr8t5mMKamZ1gHjt2jOXLl3P48GHq6+u5++67+fzzz3n44Yd58803xzNGIaxSv97Ie0fr2bAoUFYvL5EQ6UFXr4GS2m5LhyKEzZA5VgjQG/rILn6PtLgNNH35FX43LEahNO+j6tnyTmKC3XBzthvnKCdWZEAKNS15eC2YT9Ohw4PXU6PVnC7rpE9vtGB0Qlye2Qnmtm3buP/++7n11lvZtWsXM2fO5OOPP+bJJ5/k1VdfHc8YhbBKB8610K83sjLZ19KhWBUPF3tiglw5Vdph6VCEsBkyxwoB5yv2YTIZiXFKoONCHn5LbjD73jNlnSRETr7a/yCvOOztnOib6UXHhTx6m5oBmBPpjslkIlejtXCEQgxndoJZWlrK6tWrUSgUHDt2jBtuGPhLn5CQQF1d3bgFKIS1evtwHbfO98fZcXJ9WzoWkqJUkmAKcRVkjhVTnclkIqvgLVKjv0XLwWO4x0TjGhpq9r1nyjpImoTN5ZRKeyL8k6h2qMQ1PIymwwPbZJ0d7EiI9JBtssIqmZ1gent7U1tbS2trK1lZWSxYsACAgoICObdLTDmF1V2cLe/kjgWBlg7FKiVHqThd1onJJHWYQphD5lgx1ZU35NDcWUnK9Nto/OJL/K9i9bK2tZfGjv5J27080n8uZfXZ+C7MHLpN9l/nYQphbcxOMO+99142bdrEmjVrmDZtGomJiWzfvp2nnnqKW2+9dTxjFMLq7Dhcx8J4L0J8nC0dilVKivKgpbOfiqYeS4cihE2QOVZMdVkF25kTcRNGTSO9jY34Llpo9r05xR0EezsR4Ok0jhFaTmTAXOpai3BPS0RbWERPfT0A82PV5FZ0otXpLRyhEEONeEzJpe677z6ioqIoLy9nzZo1APT29vLII4/w7W9/e9wCFMLatHf3s/tkE9vuj7N0KFbLx8ORcD9nTpd2EOHnYulwhLB6MseKqaypo5zi2mP8aNWrNP5zD15zU3BQqcy+P6ugjflxnuMYoWX5q6NwdVJT71CLW9Q0mg4dIfSO24kPdcfDxZ6swnaWJfpYOkwhBpmdYAIsXryYxYsXD/7+3/7t38Y8ICGs3c7jjQR4OpIWo7Z0KFYtaZqKU6WdrJsfYOlQhLAJMseKqSqr4G2iAufh4xpG6aEjTP/xA2bfazSayCps57/XR41jhJalUCiJ8E+mvP4ks/+1TTb0jttRKhWkx3lyJL9VEkxhVcxOMCsrK/ntb39LYWEhOp1u2Ov79u0b08CEsEYGo4kdh+u4c3EQSqUcTTKSlOkq/ry7ApPJJMe4CDEKmWPFVNXV08o5zW7WL3yG1uwcTCYT3vPmmn1/fnUX2h49qZP8S99I/7mcKHqHGxfejea1N9BV1+ASEsyCeE+e/1gjc62wKmYnmI888ggtLS3cfvvtuLtPrkNshTDXkfw2WrX9rEn1s3QoVi81RkXdm31UN/cS6iu1qkKMROZYMVUdL3wbb/cwogLmkffXrfgtykTp6Gj2/ccK2pgV7oGHy1VtyrM5kQEp7Mr5X/pVStxjY2g6dJiwDetJj/VkS2cxRTXdxIa4WTpMIYCrSDDz8/N59dVXSU5OHs94hLBqOw7VsjrVD/dJPpGNBX+1ExF+zpwoapcEU4hRyBwrpiJdXwcnit5lbdrj9LW00ppzioRfb72qZ3yZ28KNsyd/p2Vv9zA8XPzRNJzEd2EmDfv2E7ZhPZ7uDswKd+dIfpskmMJqmN1FNiIigt7e3vGMRQirpmnUkVXYzrcz5WgSc82TFupCmEXmWDEVHS/cgcrVn/jQG2n84gCu4WG4R083+/7q5h7yKrumRP2hQqEgMiCF8vqT+C5Ip1tTga66BoAFMzw5nNdq4QiF+JrZCeYTTzzBM888w9GjR2lvb0er1Q75EWKye/dwHSnTVUwPdLV0KDYjNUZNTkk7RqOchynESGSOFVNNT5+W44U7WDhzI6Cgfs8+ApYvu6o6wv1nm4kJdiV8inQrj/SfS3lDDk5+frhHT6f5WBYAC+K9yNV00tEtx5UI62D2Pr/vfe97ANx///1D/vJfLCrOy8sb++iEsBLdvQY+PtHIkxvM/2ZVwNxoFW1dekrquokJlq07QlyJzLFiqjmc9xoeLr7MDFtKR+55epua8Lth8eg3XmLfmeYpsXp5UWRACh8f/wWt2hp8MtJpPppF6B23MyPEDS93Bw7ntXLzXOkRISzP7ATztddeG884hLBqu3IacXe2Y9GsyV/nMZbUrg7EhbhxoqhdEkwhRiBzrJhKWrTVHC/cwXcWPoNSaUf93n34pM/HQeVh9jNKarvJq+riF/fGjmOk1kXtGoC3eyjlDSeJS5+P5vV/0NvYhJOfLzfO9mb/2WZJMIVVMDvBTEtLA6CkpITq6mrmzZuHwWCQbndi0jOZBo4muWNBIPZ20gL8aqVGD9Rh3nVDsKVDEcJqyRwrpgqTycS+079nWkAq04Pmo9d20XzkGPE/e/yqnvP+sXrSYtSE+EytJnKRAXMpr88hOWMtLmGhNGcdJ3jtapYk+PBff82ju9eAq5OdpcMUU5zZNZhNTU2sX7+eNWvW8MADD1BXV8cPfvADHnnkkcue2SXEZHGypIPKxh7Wzfe3dCg2aV6MilOlHegNRkuHIoTVkjlWTBXnK/ZSUnecFck/AaDx4EEcPNWoE+aY/YyePgO7chq5PSNgvMK0WpH+KWgaTmIymf61TfYYAMlRKpwd7TiS32bhCIW4igRz27ZtODo6sn//fuztBxY+t2zZQlFREb/+9a/HLUAhLO3tQ3WsSPbFy93B0qHYpKRpKvr0Ji5Udlk6FCGslsyxwhZUNJ7h0+xt/OPAJt45/P9xvPBtunvN7xTeqq1hd85vWJb4Y3w8wgGo37Mf/2VLUSjN/kjKpzlNODkoWTzL66rfg62L8E9B29NMc6cGn4z5dFzIo7+9HXs7BYtne/HF2WZLhyiE+Qnm/v372bRpE8HBX29zi4+P5/HHH+fzzz8fl+CEsLSalh6+zG3hO3I0yTVzcbJjToQ7WQXyraoQVyJzrLBm/foePjz2NG8c+E90fR1E+Kegdg0gp/gDfvfR7ew9/Xt0fR0jPkPX18FbXz1KhH8KqdG3A9BZVExXWRkBy5aaHYveYOK1/dXcfUMw9nbmJ6WThZuzF/7q6ZTV5+A2bRpOvr60HD8BwNIEHw7ntdLbLzuGhGVd1WnxLi7D20C7ubnR3d09ZgEJYU22H6xjTqQHM8OlDup6LJjhxZe5LfxwZZilQxHCaskcK6yR3tDH24cep6O7gR/e9DJ+6qjB15YnPURJ3TH2n/kzZ8o+ZeHM+0iZvg4He6chz2jrquPtg4/h7OjObelPolAMJIZ1uz/De14qTn6+Zsez53QTWp1+Sm6PvehiHea8mDvwyZhP89EsAlYsZ16MGnulgkMXWqdUd11hfcz+6mfp0qW89NJL6PUDZ+woFAra29v5wx/+wIIFC8x6xokTJ1i7di0JCQnceeedlJaWDhuj1Wp5+OGHSU5OZsmSJezYsWPYmHfffZfNmzcPuVZfX899991HYmIiK1euZP/+/ea+NSEuS9uj56OsBu66IcjSodi8BfGenK/U0qrtt3QoQlilsZhjhRgPe0//gRZtNfcseX5IcgkD/59GB2Xwg5teZmnCf3Cs4E1+/8l69p/5E0U1hymuOcre03/gxd33onYL5O4bfouD/UBTHr1WS9NXhwhavcrsWHS9Bv74aQX3LAmZ0o1sIv1T0DSewmg04JORTtuZs+i7unC0V7I8yZdPsxstHaKY4sxOMH/2s5+h1WpJT0+nv7+f733veyxcuJDm5maefPLJUe/XarU89NBDbNy4kYMHD5KWlsamTZuGjdu6dSt9fX3s2bOHbdu28cwzz5CbmwtAQUEBL730Etu2bRt232OPPUZ4eDgHDhxg8+bNPPLII9TX15v79oQY5qOsBjzd7VksR5Nct+ggV/xUjhyTbbJCXNb1zrFCjIey+hxOlnzAHQuexsPlyquMSqUdydNv4cE127lxzr9T11bEx8e38mHW09S3FXFr2s/4zsJncHRwHbynft8XOPr6XFVzn7/vr8bRXjnlv/gN90uit7+L+rZiPOJisfdwpzXnJABr5vlxJL+Nlk75QldYjtlbZFUqFS+//DKnT5/mwoUL6PV6oqOjzf5mdd++fYSFhbF+/XoAHnzwQf7+979TVFRETEwMAH19fezcuZN3330XX19ffH19WblyJZ988gmzZ8+msLAQjUZDUNDQf1jq6urIzs7mhRdewMPDgxUrVpCQkMDevXu5++67zX2LQgzq1xt562Atdy0Oxk4pR5NcL4VCwYJ4TzkEWogruN45VoixZjTq+fzU75gX822CvWeYdY+9nSPJUWtJjlo74jiT0Ujdrt0E3rzK7OY+FY063viihm33x+FoP/VqLy/l7OhOkFccZfXZBHnH4TM/jeYjx/BbvIjZ4e6E+Djx2akm7lw8tRNxYTkjJpgzZsxAoTDvw3VeXt6Ir+fn5zNz5szB3zs6OhIZGYlGoxlMMMvLyzEajURHRw+Oi42N5fjx4wDccsst3HLLLbzwwgtoNJohf3ZoaCgeHh5D7isvLzcrdiG+6cPjDfTpTdyWLkeTjJXMeC+efqsYg9EkSbsQjO0cK8RYy6v6ks7uBhbNum/Mn9125ix9zS0ELFti1niTycSzH5STMcOTBfFTr3Ps5UwPSqekLosF8Xfjk5FO3i9/haG3FzsnJ9ak+vHJiQZJMIXFjJhgvvbaa4O/Pnv2LH/5y1/4wQ9+QEJCAkajkZycHLZv385///d/j/oHdXZ24unpOeSam5sbWq12yBiVSjVsTFfXyMcbXOm+hoaGK96zfft2tm/fPuSak5PTsNpOMfX09Bt4ZU8V9y8Lwdlx6tZ4jLV5MWq6+4yc12hJmOYx+g1CTHJjOccKMZZMJhNH8/7B3OjbcXYc+3+v63btxnfxQuzdzWug99X5VnKK23nr/ySNeSy2KjooncMXXqO3vwvV7Fko7O1pO30Gn/lprJ7rx593V1JQ1UVcqJulQxVT0IgJZlpa2uCvf/GLX7BlyxZWr149eC0jI4OIiAj+/ve/c/PNN4/4B6lUKnp6eoZc0+l0qNXqwd+r1ephB0p/c8zlXMt9GzZsYMOGDUOuGQwGTp8+PeKfJSa/d4/Uo1AouC196naoGw+uTnakRKk4nN8qCaYQjO0cK8RYqmrOpaG9hO/e8L9j/uyehgZaTuSQuO0Z88b3G3j2w3L+bWkIIT7OYx6PrQrymoGzowdl9dnMCL0B73lzaTmWhc/8NAK8nMiM9+KdI3X87DvTLR2qmILM3sReVlZGWNjwIwYiIiLM2roTHR09ZFxfXx8ajWbIttmQkBAMBgOVlZWD14qLi4mPjx/x2VFRUZSXlw9JYM25T4hvauro46+fVfHAzWE4OUztGo/xkDnTi4PnWy0dhhBW53rnWJBO7WLsnC37lJjgTNydx77JXe0nu/CIi8U92rzE57V9NSiAe5cGjzp2KlEq7YgKnE9xzVEAfNLn03IiG5PBAMD6zAB2n2yio1tvyTDFFGX2J+i4uDj+8pe/0NfXN3itr6+Pl19+ebCGciQrVqygoKCA3bt309nZyW9+8xuSkpIICPh6lcjFxYVVq1bx7LPP0tnZyeHDh/nss89Yu3bkYvGwsDASExN57rnn6OrqYufOneTl5bFkiXl7+4W46LcflhMb4sqaVGlEMx5unO1NcW03mkbd6IOFmEKud46VTu1irPTre7lQuZ/EaatHH3yV9N3d1H++l5B1t5g1vrq5h9e+qOa/1kXi7CAlK98UHZROce1RTCYTnslJGHv7aD9/AYD5sZ74qx3ZeeLK5WJCjBezE8ynnnqKM2fOsHjxYjZu3Mj999/PokWLyM7O5umnnx71fg8PD1544QWee+45MjMzKSwsZOvWrcDAxJqVlQXAE088QW9vL5mZmWzZsoWnn376st/qftOvfvUrzp8/T0ZGBi+++CLPP//8kKY/Qozm4+MNfHW+lce/HWV24w1xdYK8nZgZ5sYXZ1ssHYoQVuV659hLO7Wr1WoefPBBNBoNRUVFg2MudmrfvHkzvr6+pKamDnZqB0bt1P7oo4/i5eU1pFO7mHzKG3IwmUxMD5w/5s+u37MPB5UH3mnzzBr/7AflzItRs2iWNPa5nKjANLp6W6lvK8LO2RnP5CRajg18nlYqFdyxIIB3DtdhNJosHKmYasw+piQ+Pp49e/awa9cu8vPz0ev1rFy5kltvvRVXV9fRH8BAPcnu3buHXS8oKBj8taenJ3/84x9HfM5PfvKTYddCQkJ4/fXXzYpDTG5anZ7q5l6USgjxcTbrMOZcTSe/freUx74dxbQA8/5/FtdmaYIPe880c9+yEEuHIoTVuN45Vjq1i7FSVHOY6UHzsbNzGNPnmgwGand+QvC6W1HYjT4vHyto42hBG289mihf+l6Bq5OaEO9ZFNceJdArFp/0+Wje+CfTfvh9FAoFa+f586ddlWQVtpExQ5J0MXHMTjBhYMJat24d69atG694hLhmJ0s6eHV/NVkFbVz8ss5OCbPDPVid6seKJB/cXYb/L3+iqJ3HXy1g/cJA1s6TY0nG29IEH37/SQXVzT3SsEGIS1zPHCud2sVYMJlMFNUcZknCA2P+7Oajx9B3dZt1NIneYOK5j8r57qJAwv1cxjyWySQ6OJ3immMsnLkRr3lzKXrhD2iLS/CIiUblas/Nc33ZfrBOEkwxoa4qwRTCGukNRn7zQTkfHKvn1vkBvPzwHGJDXDGaoLi2m69yW3h5bxXPflDGolne3DjHm3A/Z9q79Hx+uolPs5u4f3kIP7wp1NJvZUoI9XUmNtiVL862cM8SadogxFiQTu1iLNS1FqDtaSE6KGNMn2symaj+4GMCV67AzmX0hPGDrHpatf18b7nMy6OJDsrgy9y/0d3bjquHGvXsWTQfPYZHzMBOhQ0Lg7jrN2fQNOiI8JdkXUwMaZMpbJreYOLxVws5dKGVlx+ewxPfjmJmuDv2dkoc7ZXMDHPngZvD+eBnKTz7g3icHJQ8/7GGf/vtOTa/UkCrVs+LP57Fv68Mky04E2hpog+fn26ydBhCTBrSqV2MhdL6E4R4z8TVaeQvHa5W+7lcusrKCFo7euMgrU7Pi7sq+dGq8MvuOhJDBXjG4ObkRWndQO2lT/r8wTpMgOlBrqRGq3n7UK2lQhRTkCSYwqb97uNy8iq1/OWh2cSHXfnAZjulgnkxarbcGc3OJ+dy5Nfz+WprGs9+f4acyWgBq1J8ya/qorSu29KhCDEpSKd2MRYqGs8Q7p805s+t2vEu/suW4uTjM+rYN7+qxcvdgVvTpGTFHAqFgpjgBRRWHwLAOz0NXXUN3ZVVg2O+uyiQnSca6dTJkSViYkiCKWxWVkEb7xyuZ9v9Mwj0crqqe+3tlLJiaUHB3s6kTFexK6fR0qEIMSlIp3ZxvYxGA5WNZ4nwG9sEsyMvn/bc84TecduoY7U6PW8drOV7K0Kwt5M52lxxoYsprj2K3tCHk48P7jExNF+yipkZ74WPhwMfHZcjS8TEkL0HwibpDSZ+/V4Z9y4JZmb4lVcuhfVaPdePlz6r5IGbw7FTygcJIa6XdGoX16O+rZh+Qw+hvnPG9LlVO97B/8bFOF+ymn4l2w/V4e3uwIok3zGNYbKL9J8LKCivzyE6OAOf9DSajx4jbP0dwMCRJd9ZGMSbB2v57qIgmXPFuJMVTGGTPsluoFOnZ+NSOerCVi1N9Ka9q5+c4g5LhyKEEFNeReMZAj1jcHJwG7NnaotLaD11htBv3zH6WJ2ef35Zw/dXhEoCdJXs7RyJDsogv/orALzT56MtLqG38eteB2vT/Ojo0nPwfKulwhRTiCSYwuYYjSZe21/DPTcG4+Y8+llawjq5O9tzwxxvPsmWLTtCCGFplU1nCPNLHNtnvv0OvpkZuISM3jF8+6E6vNwdWJEsq5fXIi50MUXVhzAaDbiGhuASFkpz1vHB192d7Vmb5sd2afYjJoAkmMLmZBd3UN/Wy7p0aQBg626Z58/+M810dEvjASGEsKSalnyCvceuM7C2uISWE9mErv/26GN79Lz5ZQ3fWy6rl9cqOiidnn4tVc25wPBusgDfWRjIyZIOimpGPvtWiOslCaawOR8cq2dZog9qVwdLhyKu07wYNb5qRz7NlmY/QghhKV09rXR01xPsPWPMnql5/R/4LV6IW0T4qGPfPlSH2s2Bm2T18po5ObgRGTCXgqqBbbI+6fNpP3+B/o7OwTFhvi5kxnux/WCdpcIUU4QkmMKm9PQZOJTXyuq5fpYORYwBpVLB7ekBvH+sHpPJZOlwhBBiSqprLcDJwR0v97Hpa9B+Lpf2c7mE37lh1LFdPQb++WUt31sRKp1jr1NcyGIKqr/CZDLhNj0KJx9vWk6cGDLmu4sC+exkI23afgtFKaYCSTCFTTma34ajnYK50SpLhyLGyNp5/lQ29XC6rHP0wUIIIcZcTUs+gV6xKBTX/7HQZDKhef2fBNy0AufAwFHH7zhch9rVnpWyennd4kIW0d5dT11rIQqFAu/5abQcOz5kzLwYNcE+zrx/rN5CUYqpQBJMYVO+ONfC4tne2NvJ/7qThbeHA0vmePP+UZnshBDCEmpbC8Zse2zriWy6ysoI+87otZddPQb+caCG+5fJuZdjwc3Zi0j/FC5U7gMGtsm2nT6DQacbHKNQKNiwKIh3j9SjNxgtFaqY5ORTurAZJpOJ44XtLIj3snQoYox9KyOAfWeaZcuOEEJYQG1LAUFecdf9HKNej+b1fxC0djWO3qPP1e8crsPdxY5VUvYyZmaGL+d8xT5MJiOqmfEonZxoPXl6yJjVc33p6TOw/2yLhaIUk50kmMJmlNbraNH2M3e6bI+dbFKmqwj2duITafYjhBATStfXQaeugQDPmOt+Vt2u3fR3dBK63oxzL3v0vH6ghu8vl9rLsTQjdDHanmaqmnJR2NnhnTaP5m90k3V2tGNdegBvHZQjS8T4kART2Izsonaig1zxcpfusZONQqHgtowA3jsqzX6EEGIiNXVosFM64OU++lmVI+lra6fize1EbrwHe1fXUce/fWig9lJWL8eWi6OK6YHzOV9xcZtsGq05ORj7h+4QWp8ZSF6llvMV0v9AjD1JMIXNyC5uJzVGbekwxDhZk+pHfVsvJ0s6LB2KEEJMGU0dGrzdQ1Eq7a/rOZrX38A1NBS/G28YdaxWp+efB2r4vnSOHRezwpeRV7kfo1GPZ1IiJoOR9nO5Q8YEejlxw2xv3vpKjiwRY08STGETDEYTJ0s6mCfdYyctTzcHlib4SLMfIYSYQE3tZfiqIq7rGZ2FRTTsP0DUv/8AhXL0j5ZvHazFy13OvRwvscEL6dV3o2k8jdLREa+UZJq/0U0W4LuLgth7ppnG9j4LRCkmM0kwhU3QNOjo1BlImOZh6VDEOLotPYD951polWY/QggxIZo6NfioIq/5fpPRSOlLfyVg+VLco6ePOr6ls59/fFnLD24Kk9XLceLo4EpM8AJyNXsA8MmYT8vx45iMQ7vGJk7zICbYlXeOyCqmGFuSYAqbcKFSS6iPE2pXqb+czJKjPAj1ceaTE9LsRwghJkJTh+a6VjAb9u1HV1NLxD13mTX+L59XEunvwookn2v+M8Xo5kSsJK/yC/r0OrzmpqDv1NJZUDhkjEKh4M7FQbx3pJ6efoOFIhWTkSSYwibkVWqZGeZu6TDEOFMoFNyW7s/7x6TZjxBCjLd+fQ/tXXX4XuMKpl7bheb1fxBx93dxUI/eI6G0rpsPjtWz6dYIlEpZvRxP04PScbBzoqDqK+zd3PBMTqLp4KFh45Yn+uBgr2BXdpMFohSTlSSYwiacr9AyM1wSzKlg9b+a/eRIsx8hhBhXzZ0VAPh4hF/T/RVvvoWDpyeBq1aOOtZoNLH1nVKWJviQOE36KYw3O6U9cyJXcqbsUwD8Fi2k6dARTIahK5UO9kq+szCItw7Wyhe7YsxIgimsXr/eSFFNt6xgThHS7EcIISZGU0c5nm6BONg7XfW9XZoKaj/dPdDYx85u1PHvHKmjrF7Hf90WeQ2RimuRELma8oYc2rrq8E5LxaDTDesmC3B7hj81Lb0cK2izQJRiMpIEU1i9otpuDEYTcSFulg5FTJDbMwL44lwLLZ3S7EcIIcbLQP1l5FXfZzKZKH3pr/hmZqCePWvU8ZoGHX/4pIJHbovEx8PxGiIV18LfM4ogrxmcLd+FnYsL3mnzaPxq+DZZtasDa1L9+OeXtRaIUkxGkmAKq1dQ1UVkgAsuTqN/Qyomh6RpHoT5OrPzRIOlQxFCiEnrWhPM5sNH0BYVE3nfxlHHdvUY+D9/L2DRLC9WpcixJBMtcdrNnC37FJPJiO/ihTQfO4axf/iXt3cuDuJ4UTsltd0WiFJMNpJgCqtXVt/N9EBXS4chJtBAs58APsxqkJoQIYQYJ00d5VfdQdbQ00PZy68S9p1v4+Q7cifYnn4Dj76Sj51Swc/WT0ehkMY+E21W+Ao6dU1oGk/jlZIMKGjNOTlsXIS/CwvjvXh1f/XEBykmHUkwhdUrrdMRJQnmlHPzXF/qWqXZjxBCjAeDUU9LZ+VVr2BWvfMeSidHgtfdMuK4Nm0/P/1rPo3tfTz/7/GyC8lCXJxUzAi9kZMlH6J0cMAnY/5lt8kCfP+mUD4/1UR5vW6CoxSTjSSYwuqV1XczLcDF0mGICXax2c8Hx6TZjxBCjLVWbTVGk+GqVjB1tXVUv/8h075/P0qHy5/P+kxAAAAgAElEQVRLbTKZ+OJcM/f+9iz9ehN/+vEsfFVSd2lJc6NvI7/qS7p6WvFbtJDWE9nou4cnkTPD3MmY4cnLe6ssEKWYTCTBFFatU6ensaOfqABZwZyKbkv354uzLbRppdmPEEKMpaaOctydfXB29DD7nrK/vYJnciLeqXOHXNcbjFyo1PLa/mru/N8zbPlnMevm+/On/5gpyaUVCPNNwMc9jNNlO1HPmY2dmxvNR45eduwPbwrj81NNaBpkFVNcO3tLByDESMrqdNjbKQj1vfoW6sL2pUxXEejlxKc5jdx1Q7ClwxFCiEmjqUODz1WsXrZk59B26jRJL/wWTYOOU6UdFFR3UVjdRWFNN3qDkZhgN9am+nNzqq90i7UiCoWClOjbOFbwFgtm3I3/khto2LefgOVLh42dGT6wivnXz6t4+p4YC0QrJgNJMIVVK63vJsLPGXs7WWyfihQKBbenB/DBsQbuXBwkDSKEEGKMDHSQNS/BNPb3U/LXV2hJWsK9r9ZT2aRhWoALcSFuLE3w4T9WhzMzzB1XqbO0WnMiVrL/zJ8oqcsieNlSqt/7AF1tLS5BQcPG/sfN4Wx87ix33hAkZ5CLayKf2oVVK63rZppsj53S1szzo6q5hzNlnZYORQghJo3mjnKzG/wc+tu7NDd18oYime8sDGTnkyls/z9JPHV3DHffGExqtFqSSyvn7OjOrIgVnCz+ENfQEDxmxNGw74vLjo0NcePmuX4891G5dHIX10QSTGHVyup1RAVKg5+pzMvdgRtne/O+NPsRQogxYTIZaeqsMGsF842d+fR99iHdS27jjSfmsWFREP5qKVuxRXOnr6Oo9gjt3fX4L1tKw/4DmAyGy4594OYw8qu6OJDbMsFRislAEkxh1UrruuWIEsFt6f7sP9NMR7fe0qEIIYTN6+huoF+vG3UF850jdTTs2I5LaCi3/8c6KVexcUHeMwjyiuNUyUf4LlyAvrOTtrPnLjvWX+3EvTcG88LHGvr1xgmOVNg6+ZdCWK2LHWTliBKRGq3GV+3IrpxGS4cihBA2r6mjHCcHd9ydfa445nRpB2+9lUVaRy4JD/0QhVI+Mk4GqdHf4mTJh+Bkh8+CDOr37Lvi2HtuDKa338jbh+smMEIxGUzovxYnTpxg7dq1JCQkcOedd1JaWjpsjFar5eGHHyY5OZklS5awY8eOwdfq6+u57777SExMZOXKlezfv3/wtZ///OfExcUN+enq6pqQ9yXGx8UOsmG+zpYORViYUqlg3fwAPjhWL/UgQghxnS42+LlS47TefiO/2F7M93sP4X/DYjziYic4QjFeZoYvQ4GC8xX7CVx1Ey3Hsuhrab3sWBcnO368JoK/fV5FqxwXJq7ChCWYWq2Whx56iI0bN3Lw4EHS0tLYtGnTsHFbt26lr6+PPXv2sG3bNp555hlyc3MBeOyxxwgPD+fAgQNs3ryZRx55hPr6gbosjUbDq6++SkFBweCPm5vbRL09MQ5K67sJlw6y4l9uSfOjorGHE0Xtlg5FCCFsWlNH+Yj1l69/UU14UwGerdVE3Hv3BEYmxpu9nSMp0bdxonAH7nGxuIaHUff5niuOvznFlwh/F/68u3ICoxS2bsI+ue/bt4+wsDDWr1+PWq3mwQcfRKPRUFRUNDimr6+PnTt3snnzZnx9fUlNTWXlypV88skn1NXVkZ2dzaOPPoqXlxcrVqwgISGBvXv3AlBRUcG0adMm6u2ICVBaryNKOsiKf/HxcGR1qh+vf1Fj6VCEEMKmDaxgRl72tfbuft78oprb2g8TvO4WnHy8JzY4Me5Spq+joaOUquZzBK6+mfrP9mDUX77HgVKp4KfrIvkoq57CGtkZKMwzYQlmfn4+M2fOHPy9o6MjkZGRaDSawWvl5eUYjUaio6MHr8XGxqLRaMjLyyM0NBQPD48hr5WXl2MwGKipqeHxxx8nOTmZNWvWsG/flfeUC9tQVtct9ZdiiLtvCOZ4UbtMcuL/tXfn4VFU6f7Av9VL9s6+kT0kJIQlCSAgBoEAQUQQBYELysBc5yK/QUW9CoPKFUfcZgZuFOUKMijiKAGisugEFNCwL7JDVrISyB6S7s7SSXf9/gjEtCQhQOjq7nw/z8NDuup08p6crn7zdp06RW3gZSjUWR3dA3NT6lWMaEyHTZ0a/o89auLIyBRU9p7oEzgaxzO3wGvkg9A3NKDy6LF220eHqJAQ64n//Y63LaHOMVmBqVar4ezsbLTN0dERGo3mlm20Wm2H+yorKxEeHo65c+fiwIEDWLBgAV566SVkZma2G09SUhKmTJli9O+pp57qgp5SV2m+RQnPYNJvQnzs8WAfN/zrZ57FJGqNl6FQZ2nrq1Cnq4ZHG2cw6xr0SP7lMkaXHEDAtKlQcIyt1pBe05BetB+apir4jInH1R9SOmy/4JEgXCjU4OdzvG0J3ZrJCkxnZ2fU19cbbaurq4OLi0vLYxcXF9TV1bXZpqN9Xl5e+PbbbzFy5Eg4OjpiwoQJGDp0KA4cONBuPDNmzMA333xj9O/LL7/sgp5SV9DUNaG0WsczmHSTP8T7Y/epChSW1926MVE3wctQqLPKa/Ihl9nA1cH3pn27T5cjrvo07JQy9JgwXoLoyFT8PKLg7x6FE9nfwHfCeNRcuAhtXl677X1cbfGHeD98sCMfDY28bQl1zGQFZnh4ONLS0loe63Q65OfnG02b9ff3h16vR2HhbxcSZ2dnIyoqCj179kReXp5RkXpj36lTp5CcnGz085qamvjpqgXLKamDXCYgyIsryJKx6FAVhka4YA0XHCBqwctQqLPKa/LgoQqETCa/ad/3BwowquIIgmbNgMzGRoLoyJQGRzyBUzk7oPByg9t9g1D07fYO2z81yg96g4hNqVdNFCFZKpMVmAkJCcjIyEBKSgrUajVWrFiB2NhY+Pj4tLSxt7fH+PHjsXLlSqjVahw8eBC7du3CxIkTERgYiJiYGCQmJkKr1WLnzp1IS0tDfHw8lEolli9fjtTUVGi1Wvzwww84e/YsxowZY6ruURfL5Qqy1IE/TwjCT6crkFnEa8CIAF6GQp1XUZMPT5eQm7ZnXtHC+/x+2Ls6wzt+lMnjItPrHTAKSrkdzuXvQsDUx1G+/wAaytq/37SdjRzPTQzGZ3t42xLqmMn+elepVFi1ahUSExMRFxeHzMxMvPvuuwCAyMhIHD16FACwZMkSNDQ0IC4uDm+88QbeeustBAYGAgDef/99XLhwAcOGDcOaNWvw4YcfQqVSoV+/fli2bBmWL1+O4cOH47PPPsPatWvh6elpqu5RF8sp5vWX1L4If0eMjfXABzvyueAAEXgZCnVeWU1emyvI/njkMkarTyJk5jQI8pvPbpL1kcsUGBT+OI5lboGqdwScInqhaNvODp+TEOuBQE97bNxXZKIoyRIpTPnDhg0bhpSUmy8izsjIaPna1dUVq1evbvP5/v7+2LhxY5v7Jk+ejMmTJ3dNoCS53JJa9AtW3bohdVvPPRKM6X87jR9PV2DcAH6YRN1beHi40aUit7oM5cYHt21dhmJnZ9eyb/LkyTh16hRycnIwderUlu/Fy1AsV3lNPgaETTLaJooiru39CQpHR3g9OFyiyEgKg8Iew8G0jci6cgj+jz+GzJWJCJzxBJSqtv8GEwQB88YH4tUvMjFrpB88nTmVmm7G+YdklngGk27Fx80W/zUuEP+7LQ+aurbv30XUXfAyFOqMhsZaqOtK4akKMdp+4VIlBl05gsBpU3j2spuxt3XGwJ6P4mDaRrjdNxC2Xp64uvOHDp8zPMoVvXo4YMNensWktrHAJLOjqecKstQ5/zHCF66OCqz+oUDqUIgkxctQqDMqavIhCDK4qwKMtp/d8gPkSgWCHxotUWQkpaGRM3C1KgOFFecQOGM6rmzficYadbvtb5zF/OZQCUquNZgwUrIUJp0iS9QZuTdWkPXkCrLUMYVchtemh+G/PrqAUf09MCTC5dZPIrJSvAyFbqWsJhduTv5QyH+b1qjX6eD26x6I8RMgUyoljI6k4uzgjf7B43Ao/Uv8x/C/4fKWrSj69juEzJnd7nOGRrigT5ATPt9ThMVTe5owWrIEPINJZie3uA5BXnZQKvjypFvrF6zCH0b74a2kbE6VJSLqQHlNHrycje9nev7b3YBBj/tmPiJRVGQOhvWehUtXj6K0JgdBT87E1Z0/QFdV1W57QRDwp4QA7DhWivIanQkjJUvAv+DJ7OSU1HJ6LN2WPyUEwNlBgZXb8qQOhYjIbJVV58LL5bcCU9TrUblzO9JD4+DuzkWbujNP5xBE+j+Ig2kb4T5kMByCg3A5+dsOnzMkwgU9fR2QtJ/3xSRjLDDJ7OQU16KnDxf4oc5TKmRYNjMcu06WI/V8pdThEBGZpbLqXKNblJSl7oehthauYxKkC4rMxvA+c5BWuA+l1ZcQ9ORMFP97F+pLStptLwgC5oz2x9ZDJZxBREZYYJLZySmuQ1gPFph0e3r5OWLeQ4F4Z0sObwBNRPQ7usZaVNcWt5zBFPV65G9Oxj7ngYiL8ZU4OjIHPdwjERUwCj+fWwvX2Bi4RPdH3mcbOnzOqP7ucHdSIvlQ+4UodT8sMMmsqOuaV5AN4y1K6A48OcoPfu62WPFdrtShEBGZlfKafAiCHB6qIABAxZGjaKioxMWAIejlx5xLzUb2/xOyrx5FYflZhD49F5XHTuDa2XPttpfLBMyO98PX+6+ivlFvukDJrLHAJLOSU1wLpVxAAFeQpTugkAt4fUYY9p2txP4LnCpLRHRDWU0u3K+vICuKIgo3b8Wl0PsxqH8PCIIgdXhkJjxUQYgNfQT7zq6Bvb8/ejzyMHLXrYeob794nHCfF2QC8P3xMhNGSuaMBSaZlUvFdQj2todCzmRHd6anrwP+MyEA723NgZrXhBARATC+/rLq+AnUXy1GstAfcVFu0gZGZufBvn9E8bUsXCzci8AZ06GruobilN3ttrdRyDBrhB827ruCJr1owkjJXLHAJLNy6Wotp8fSXZsz2g9uTkp8sD1f6lCIiMxCWU3zCrLNZy+TIRs2ChUGO94/mG7i7OCFB/vMxY+nPoTeFgiZMxv5X36FhvKKdp/z+DAfqOuasPds+22o+2CBSWYlp7iWC/zQXVPIZVg6IxzfnyjDiexqqcMhIpJcWXUOvFxCUX3mLLR5eTjtfz8GhjnDwVYudWhkhoZGTIedjQqp59fDe0w8nMLDcOmTtRDFts9QOtrJMS3OFxv2FrXbhroPFphkNkRRRPbVWoT58h6YdPciAxwx40Ff/P2bXDTpDVKHQ0QkmdqGatTUlsLbJRyFSVvgM3YMfikQOT2W2iWXK/HwoP/G8aytKKq4gPAF81F95izKDxxq9zkzHuyBgrJ6HE6/ZsJIyRyxwCSzUalpRHVtE3pyiix1kT+NC4C6rgmbUoulDoWISDLFVZlQyu2gyK+BOjMLtmMfQfbVWsT1cZU6NDJjwd4DMCj8cWw7+hZkHs4Iemomcj9dh8bqtmcGuTkpMXmoNzbsLTJxpGRuWGCS2bh0tQ72NjL0cLOVOhSyEk52CiycFIJPdxei5FqD1OEQEUmi5FoWfFx7oWhzMrzHxONIqQzBXnYI9OSMIerY6Oj/B7lMid2nV8Fv4iOw69EDWas+bnca7JMj/XA2T42zuWoTR0rmhAUmmY3MIi3CezhAJuMKstR1xg3wQJ9AJ3y4gwv+EFH3VFyVCf8ab9RcTEPA1Ck4eLEKcX04PZZuTamwxWP3v4FzebtwrmA3Il56ATUX01D8Q0qb7Xu422LcAE+exezmWGCS2Ui7rEFUoJPUYZCVEQQBL08Jxd6zlTiWyQV/iKj7Ka7KgtPhSnjFjwTcPHAiq5rXX1Kn+br1woT7XsH3J/6OSmUlwuY/g9zPNkCbX9Bm+z+M9seBtCpculpr4kjJXLDAJLORflmL3gGOUodBVijM1wEzR/jiH9/morGJC/4QUfeha6pDY+4VGLKLEfDEFJzIroFCIUNsqErq0MiCxIQ+jIFhj2LLgVdhP6QPvB4cjoy//QNNtTcXkWG+Dniwjxu+2MezmN0VC0wyC5q6JhSW1yMqgGcw6d54OiEQ2vomfJ16VepQiIhMprgqEz3PqeD5YBzse/TAwbQqDI1wgVLBPwHp9oyNfRbuqgAkpb6CgKefhEypRFbiKoiGmz+4nTPGH7tPlSO/tE6CSElqfHchs5BepIWtUoZgby44QPeGo50cL0wOwT9/vIySKi74Q0TdQ+HhPXAtUSL4yZkQRREH06ownNdf0h2QyxSYFvc2mgyN+O7k2+i1+L9Rc/EiLm9Jvqlt/2AVhvV2w9pdhRJESlJjgUlmIa1Qg0h/RyjkXOCH7p2xMR7oF6xCIhf8IaJuQDQYoNt5AvrBAbDz8cGl4lqUXNPhgd4sMOnO2NmoMHPEP1B6LQd7Czcg4uWXUJi0BeWHDt/Udv7DgdhzpgIZl7USREpSYoFJZuFiIa+/pHtPEAS88ngofjlfiSMZvBE0EVm38oOHIa+oh/fjDwMA9p6pRHSwCu4qpcSRkSVzdvDGzBH/QPrlX3BGfgw95/0JmSs/QPWFC0btIvwcMTbGA//377YXAyLrxQKTJCeKIs7k1iCGCw6QCYT42GPWiB74x7e50HHBHyKyUgadDrlfbEBBlBZBoUMhiiJ+OlOBsbEeUodGVsDbtSemD38PRzK+Rn5oFfwfn4y0t9+DNs94htAzDwfieFY1DqVVSRQpSYEFJkmuqKIB5TWNGNDTWepQqJv4z4QA1OsM+OoXLvhDRNap6NttaGqsh3qwKxzt3HCpuBb5ZXUYHc0Ck7pGsHcsnnjgbew5+3+ovN8Jng88gAvL/orawsstbQI97fHkKD+s+C6PH+p2IywwSXKncmoQ4GELT2cbqUOhbsLBVo6XHmte8Keool7qcIiIulR9SSkub/0GVaM8Eeg3EADw0+kKxIaq4OXCXEtdJ9xvGCYPfR3//vUf0E2KgGtsLM6/9j9G98j84xh/6JoM2LCHty3pLlhgkuROXqpBLM9ekonF93fH0AgXvLkpG3qDKHU4RERdQhRF5Hy6Dqq+UUh3zkCYb/P02B9PV2BMDM9eUtfrGzQWDw18EduOLYds2mC4DxmM86/9D9RZ2QAAe1s5/vJET3y2pwgXCzUSR0umwAKTJGUwiDiScQ33R7pKHQp1M4IgYMm0nsgvrcNXv1yROhwioi5Rtu9nVJ+7ANWMMahv0iDUZxBO5ahRXNWAcbGeUodHVmpQ+GMY2e9pJB9+HTbTH4DXqBE4/+pSVBw+CgCIi3LDo0O88ca/slCv00scLd1rLDBJUhlFWlRpGjE00kXqUKgb8lDZYMkTYfjk34W4WMBPVYnIsjWUlSHn0/UI/eMc5OnTEeDRF3Y2Knx3pATx/d3h6sTVY+neeSDqKQyOeAJJBxbDYepwhMydjYy/r0Dh5q0QDQYsnBQMQRDw5qZLMHDmkFVjgUmSOpR+Df1DVHBxYNIjaYzq744pD/hg0ecZKK/RSR0OEdEdMTQ1IfN/P4SqdyS8x43FxYK9iAqMxzVtI/aercBjw3ykDpG6gfj+z6B/8Dh8ue95GO4PQdTSV3Flx/e4sOwtyGtrsOLpSBzPvIZ1uy/f+puRxWKBSZJpviakHCP7uksdCnVzCycFI8jLDq98lgFNfZPU4RAR3ba8zzagvrgEvRY+h7LqS6hQFyAqMB5bDhTD38MOg8K41gHde4Ig4KGBLyIm9BH86+cXoAmQITZxBSCKOPXcC7A5fQjv/aEXvthXhC/2ctEfa8UCkySTWVSLvJI6PDSQ14SQtBRyGd6dE4HGJgNe/DQdtQ28PoSILMfVf+9C8a4f0XvJIti4uuBM7g8I8R4IQXDFpv1X8cex/hAEQeowqZsQBAFjY5/F4F5T8dUvL+GyLhN9ly1F0JMzkbv+c9h+tgJ/G2ePtbsKsTalEKLI6bLWhgUmSWbH8VIM7uXCJdPJLLg4KPHR/D7QNugxf/UFlFQ1SB0SEdEtlaUeQO6n/0Tkf78AVa9wNDRqcTr3ewzuNRVJ+4vh6qhEAhf3IRMTBAHx0c/gwb5/RNL+xTh+KRm+4x/CwI8/hK23F8SPluPvyj34+aezeG0jF/6xNiwwSRLXNI3YfqwU0+J8pQ6FqIWroxJrF/SFh0qJOYnn8Mv5SqlDIiJqV/Gu3chK/BBhf54Pj2H3AwBOXtoGB1sXONnfh8/3FOHPE4Igl/HsJUnjgagnMTVuOfad+xQ7jr0LONki4sWFiE1cATcnGyzI/gz9f1qLN5duQXqBWupwqYvIly1btkzqIMyFKIooLi6Gr68vZDLW3vfS+p+KcE3bhBceDeG0HTIrNkoZEmI9oTeI+Ps3ucgprkXfICc42SukDo1a4fu15eGYdR1DYyPyNmxEYdIWRLz0ArxHjQAAaOurkHxoKeKj/x8+2A74udvizxOCmGdJUp7OwYjwi8OxrC04kf0NfN0i4OUXCc/hcfAcHgen2ir4/roLJSm7kH6xCN5+brDzcOfrVkJ3+37NArMVJj/TuHS1Fm9uysZr08MQ7G0vdThEN5EJAgb0dMboaHf8dKYCq3bko7q2CRH+DnCwlUsdHoHv15aIY9Y11FnZSH/3b9BmX0LU66/CbWAsgObf7/ZjyyGXKZBZ9DiOZdbgg3lRcLTjh2MkPUc7d8SEPoKaulL8+9eVqKgpgJ97b6g8/eAxeBACJk5AucEOJb+egW5nMgp//BlNZaUQZDLYeHhAkDP3mtLdvl+b9B3++PHjmDhxIqKjozFz5kzk5OTc1Eaj0eD555/HgAEDEB8fjy1btrTsKykpwdy5cxETE4OHHnoIe/fubdmXlZWFadOmITo6GpMnT8bp06dN0ie6Pdc0jfjLhgwkxHogLspN6nCIOhTq44DV8/tgxdO9cSa3Bo++dRJ/3ZSNzCtaqUMjuglzrHXTZF9CxopEnF20BI6hIYhNXAHn3pEAmv8Y/PncWuQWn0BN3X/hm8Ol+NvcSHg6c40DMh9KhS3GDXgefxr3T2jqK/Dx9zPw3ZG3UFyVCbm9HYb/YRKe+Of7yJrzJr5VxODokWycX/4ejj45Bxf/+jYKtySj+vwF6Bu4RoK5E0QTLd2k0WgwZswYvPzyyxg3bhzWr1+Pffv2Yfv27UbtXnvtNVRUVGD58uXIy8vDM888gw0bNqBfv36YO3cugoKC8OKLL+LEiRNYtGgRUlJS4OnpiQkTJmDSpEmYPXs2du7ciY8//hg///wzbGw6/+aq1+tx+vRpxMbGQs5PSrrchQI13vgqG25OSqx6Jgp2Sv6OyXKIooiTl2rwdepV7L9YhV49HDB+oBeG93FDsLcdp/KYGN+vjTHHWh9DYyO0uXm4dvoMKo4chTY3Dx5DhyDgiSlwCg9raVetLcZPpz9G5pUjKCj7M3JLAvH+3AjEhPK2JGTeCsvO4nDG18gsOgBP52BEBcYj1Oc++LlHobxGxJc/X8HOw1cwSF6CkXYl6KEuRFN+LkS9Hg4hIXAMCYZDcCAcgoLgEOAPG3d3nunsInf7fm2yAnPbtm3YuHEjtm7dCgDQ6XQYPHgwtm7dil69ehltS05ORnh4OADg1VdfhYuLC+bMmYOxY8fi8OHDUKlUAIA5c+Zg3LhxiIyMxMKFC3HgwIGWP/LGjh2L119/HaNGjep0jF2R/MprdNA26IFWv9Ubv2Gx1cbftrVyu+1a/4zrG423/f6LjtsZvRI6GYvYxg+5sU1vACrUOhSW1+NYZjXO56vx2P0+eGFyMItLsmhXKxuw+3Q5dp0sR/bVWrirlOgX5IQgL3v4e9jCxVEJlZ0cjnZyyGUC5DIBMlnz1FuZIECQAd29HHVzUkJ1F9e1slgx1l1ybKW6EaU1ZWhs0gJiq5xmEGFQqyHW1eP6juu5yNC8XxSBVm1x45HYar/4u+RqaJ0zf9snXv+ev/0M0SiBtv5ezV83f7PWfwsI158n6pqAhgYI9Q1AfQOg0UKoqgaqqiGUVQCiAaKfL/RR4TD0i4To5gKDoRHahgpUaa6iUn0RtQ1noanviV+zp2JMbD8881Ag3FXKO/r9EklBXVeOtMK9SLv8C65UXAQgwMslBO6qQDjY+CG3RI4LBUBuqRwqpS1ibBoQ1HAN7toa2FdVQlFeBqGhAZDJAGcVBFcXyFxdINjbQeboADjYQ7C3h2BrAygUEK7/g0IOyBUQFHJAEK4n5uvZWfjd/ze+vtGmdVuJCQ72kDk5wlahgp2NCwDA2UEBV8c7fx+42/drk03MT09PR58+fVoe29jYICQkBPn5+S3JLy8vDwaDoSXxAUBERASOHTuGtLQ0BAQEtCS+G/vy8vIgiiJ69+5tdAbhxr72JCUlISkpyWibra0tXn755TvuY32jHk+8dwq1DYY7/h73ktEx0sa2G1tbjqk2nmvcvOP2AgS4q5Twc7fFsN6uWPofYQj24jWXZPl6uNtizmh/zBntjwq1Dqdy1Egv1KCwvB7Hs6pRU9cETV0TtPV6GHh7rzYNCnPG//25r9RhWI3ukGMBYPbKMxjQ839gZ1Nz0764ZC/YNNz7K39aPngVbjxuRWjdDm3//Sm0lLowyIEmpYgmpQi90gCdrYg6Jz3qAgzQ9tGjxr0JBkU5gPPAVQG4ChhEOXSNztAb3KA3hMLN6TEM6x2NJU+4w9WJhSVZHpW9J4ZETMeQiOlobGrAlco0lFXnoFJdiErNJdjZVKNPYDWCvWvQ2NQAvdiEXOiRe+MbiIBNvQy2WhnsaithWyuHba0MihoZFI0ClDoZFDoB8kYBMoMAwQDI9K2+Fs2jULxTtaomHJ1UAXWdF/adWwQACPWxR9KiWMliMlmBqVar4erqarTN0dERGo3GqI2zs/NNbbRabbv7SktLO3xee2bMmIEZM2YYbbtRrd8pO6UcKW/ehyZ9c+oQWoDlbxMAAAuSSURBVGWWjoq2lg9LOtv+d4Vg63bGH7RY9gFDZAk8VDYYG+OBsTEebe4XRRF6A2AQRRgMIgxi89fdna2Ci7x0pe6QYwHgm1cHok63BXpDE4DmvClAgCAAhnE6iE16CLie/wQBgtC8HzJc/+RTgKzVGYob7eRC622y376+/r9M/tvrVdaSg3/LsVy0iKhrKBW2CPaORbB3x8WRKIowiHoYDE1Gs+0MBhF6gwF6UYTB0CrftpF2f5sBYWieddB6pkPL7INWMxtuzDxo+do8CEoFBiqUkMtssHRG82ULNkpp35NMVmA6Ozujvr7eaFtdXR1cXFxaHru4uKCurq7NNh3tc3Z2bnefqdkp5QA/QCSi6wRBgEIOmMtUGrJO3SXH2iplsFU6mvznEpF5EQQBckEBuYyrJJsjk5W34eHhSEtLa3ms0+mQn59vNKXH398fer0ehYWFLduys7MRFRWFnj17Ii8vzyiB3tgXHh6OjIwMo5+XlZWFqKioe9gjIiIi88AcS0RE5sJkBWZCQgIyMjKQkpICtVqNFStWIDY2Fj4+Pi1t7O3tMX78eKxcuRJqtRoHDx7Erl27MHHiRAQGBiImJgaJiYnQarXYuXMn0tLSEB8fj8GDB0Mmk+Hzzz+HVqvF+vXrIQgCBgwYYKruERERSYY5loiIzIXJCkyVSoVVq1YhMTERcXFxyMzMxLvvvgsAiIyMxNGjRwEAS5YsQUNDA+Li4vDGG2/grbfeQmBgIADg/fffx4ULFzBs2DCsWbMGH374IVQqFWQyGT766CNs374d999/P1JSUvDRRx/xmggiIuoWmGOJiMhcmOw2JZaAy94TEVkGvl9bHo4ZEZFluNv3a378SERERERERF2CBSYRERERERF1CRaYRERERERE1CV485hWblyOqtfrJY6EiIg6cuN9mssIWA7mWCIiy3C3OZYFZisGgwEAcO7cOYkjISKizrjxvk3mjzmWiMiy3GmO5SqyrRgMBjQ1NUEmk0EQhE4/76mnnsKXX355DyMzDfbDvLAf5sUa+mENfQCa+7Fx40YYDAYoFAreLsNC3GmOBazjtWsNfQDYD3PDfpgXa+hHV+RYnsFsRSaTwcbG5raf19DQYBVLrrMf5oX9MC/W0A9r6APQ3A+FgunL0txpjgWs47VrDX0A2A9zw36YF2voR1fkWH7sS0RERERERF2CBSYRERERERF1CRaYRERERERE1CXky5YtWyZ1ENagX79+UofQJdgP88J+mBdr6Ic19AGwnn5Q51nDmFtDHwD2w9ywH+bFGvpxt33gKrJERERERETUJThFloiIiIiIiLoEC0wiIiIiIiLqEiwwiYiIiIiIqEuwwCQiIiIiIqIuwQLzDlVWVuLZZ5/FgAED8MADD2Dp0qWora0FAOzevRuRkZFG//bt2ydxxO07fvw4Jk6ciOjoaMycORM5OTlSh9RpqampLbGPHz8eO3bsAADMnz/f6Pc/dOhQiSNtX3ux7t69G2PGjEFMTAzmzZuHsrIyiSNt36pVq256zUdGRmLJkiUWMxaLFy/Gli1bWh53dFyY89j8vh/tHSOAeR8nv+9HR7Ga83jQnbOWPMscKz1Lz7PMseYzLsyxnRwPke7ICy+8IM6bN08sLi4WCwoKxEmTJonvvfeeKIqiuHbtWvGDDz6QOMLOUavV4pAhQ8TNmzeL165dE1euXClOmjRJ6rA6pbKyUoyNjRU3bdokqtVqcffu3WLfvn3FtLQ08eGHHxYLCgqkDrFT2oq1oKBAjI2NFffs2SNWVFSIixcvFufPny9RhLevvLxcHDNmjJiVlWX2Y7F//37xnXfeEXv37i1u3rxZFMWOjwtzHZu2+tHRMSKKbb/2pNZWP0Sx/VjNdTzo7llDnmWONQ/WlmeZY02POfb2xoNnMO9Qamoqnn32Wfj4+CAwMBDTpk3DgQMHAACFhYUICQmRNsBO2rNnT0v8Li4uWLBgAfLz85GVlSV1aLd04sQJ+Pv7Y8aMGXByckJCQgIiIiJw6NAhlJaWwt/fX+oQb0kUxTZj3bFjB0aMGIHRo0fD3d0dCxcuRGpqKmpqaiSK9PYsXboUs2bNQlhYmNmPxcmTJ6HRaODh4dGyraPjwlzHpq1+tHeMHD58uN3XntTa6kdHsZrreNDds4Y8yxwrPWvMs8yxpscce3vjwQLzDq1fvx6RkZEtj8+cOYMePXoAAPLz85GUlIShQ4dixIgRWL16NUQzvd1oeno6+vTp0/LYxsYGISEhyM/PlzCqzhk0aBASExNbHldWVqKwsBDu7u4QBAGzZs3CgAEDMHXqVPz6668SRtq+4uLiNmNNT09H3759W9r16NED9vb2KCoqkjDazvnll1+Qnp6Op556qt3+mZPnn38eb7/9NkJDQ1u2dXRcmOvYtNWP9o4RX19fsx2btvrRUazmOh5096whzzLHSs/a8ixzrDSYY5t1djxYYN6hmJgY2NjYoLKyEn/5y1+QmpqKl156CQCgVCqRkJCAH3/8ER999BGSkpKwefNmiSNum1qthrOzs9E2R0dHaDQaiSLqPHd3d4SHhwNo/hRp1qxZiI6ORnh4OMLCwrBo0SLs378fkyZNwvz581FeXi5xxDerrKxsM9aqqiqoVCqjto6OjtBqtRJF2nmffPIJnn/++Zbjw1LGorWOjgu1Wm0xY9PeMTJu3DiLGpuOYrWk8aDbYw15ljlWetaWZ5ljzQdzbPtYYHbg66+/bvOi6hufqCYnJ2P8+PHQaDTYtm0bevfuDQBYt24d5s6dC2dnZ0RHR2P27Nlmu/iAs7Mz6uvrjbbV1dXBxcVFoohuj0ajwaJFizBv3jxMmzYNa9asQb9+/bBp0yYMHDgQTk5OmDt3Lry9vc3ik6Pf69u3b5uxnjx5ss1x+f0bsrk5e/YsCgoK8MgjjwBov3/mOBatdXRctLfPXMemrWNELpdb1Nh0FKuljQcZs/Y8yxwrPWvKs8yx5oc5tm2KexmwpZs+fTqmTJnS5r5PPvkEX3zxBVauXInhw4e3bC8rK8OmTZuwYMECyGTN9XtTUxMcHR1NEvPtCg8PR3JycstjnU6H/Px8o6kL5qq+vh6zZs2Cj48PUlJS4O3tDaB5+ohOp0NCQkJLW3Mdg/Zi7d+/P9LS0lq2lZaWorGx0eyvOdq8eTMefvhhKJVKAJY1Fq11dFycP3/eYsamvWMEsKyx6SjW8PBwixkPupm151nmWOlZU55ljjUvzLHt4xnMDsjlctja2t70T6/XY/Xq1UhMTDRKegDg4uKCr776CuvWrYNGo8H58+fxr3/9C48++qhEvehYQkICMjIykJKSArVajRUrViA2NhY+Pj5Sh3ZLO3bsgE6nw8cff2x0UOt0OixbtgynT5+GRqPB559/Dp1Oh8GDB0sYbdvai/XNN9/ETz/9hBMnTqCqqgrvvPMOJk6cCBsbG6lD7tChQ4cQFxfX8tiSxqK1jo6LSZMmWczYtHeMAJY1Nh3FaknjQTez9jzLHCs9a8qzzLHmhTm2fTyDeQcuXbqEhoYGzJ4922j7kCFDsHHjRqxZswZvv/02Vq9eDV9fXzz33HMYOXKkRNF2TKVSYdWqVXjzzTexaNEiDBo0CO+9957UYXVKWloacnNz0b9/f6Pt7777Lp5++mksXLgQGo0Gffv2xbp162BraytRpO1LSEhAYWHhTbGGhYXhr3/9K1555RVUVVVh5MiRWLx4sdThdujy5csoKipCdHR0y7b2+meOY9FaR8dFSEiIxYxNR8fIlClTLGZsOnodWdJ4UOdZS55ljpWeteRZ5ljzwxzbPkE0x2XXiIiIiIiIyOJwiiwRERERERF1CRaYRERERERE1CVYYBIREREREVGXYIFJREREREREXYIFJhEREREREXUJFphERERERETUJVhgEhERERERUZdggUlERERERERdggUmERERERERdYn/D2HbXrc9JxMXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_df = pd.DataFrame(profile.age[~(profile.age == 118)])\n",
    "\n",
    "description_pdstyle = \\\n",
    "    pd.DataFrame(age_df.describe()).style \\\n",
    "    .set_caption('Ages description') \\\n",
    "    .set_table_attributes('style=\"display:inline;' \\\n",
    "                          'vertical-align:top\"')\n",
    "\n",
    "\n",
    "fig1 = Figure(figsize=(10,4))\n",
    "axs = fig1.subplots(nrows=1, ncols=2)\n",
    "axs = axs.flatten()\n",
    "axs[0].set_title('Box Plot')\n",
    "age_df.plot.box(ax=axs[0])\n",
    "axs[1].violinplot(age_df.values)\n",
    "axs[1].set_title('Violin Plot')\n",
    "\n",
    "\n",
    "bins = 84\n",
    "fig2, axs = plt.subplots(nrows=2, ncols=2, figsize=(15, 10))\n",
    "axs = axs.flatten()\n",
    "\n",
    "age_df.hist(bins=bins, ax=axs[0], cumulative=True, density=True)\n",
    "axs[0].set_title('Cumulative distribution')\n",
    "axs[0].set_ylabel('Customers (%)')\n",
    "axs[0].set_yticklabels((axs[0].get_yticks()*100).round(0))\n",
    "axs[0].set_xscale('linear')\n",
    "axs[0].set_xlabel('years')\n",
    "axs[0].grid(False)\n",
    "\n",
    "\n",
    "age_df.hist(bins=bins, ax=axs[1])\n",
    "axs[1].set_title('Density')\n",
    "axs[1].set_ylabel('Customers')\n",
    "axs[1].set_xscale('linear')\n",
    "axs[1].set_xlabel('years')\n",
    "axs[1].grid(False)\n",
    "\n",
    "\n",
    "age_df.plot.density(ax=axs[2])\n",
    "axs[2].legend()\n",
    "axs[2].set_title('Density plot')\n",
    "axs[2].set_ylabel('density')\n",
    "axs[2].grid(False)\n",
    "\n",
    "age_gender_df = profile[~age_missing].groupby('gender').age\n",
    "age_gender_df.plot.density(ax=axs[3])\n",
    "axs[3].set_title('Density by Gender')\n",
    "axs[3].set_ylabel('density')\n",
    "axs[3].legend()\n",
    "axs[3].grid(False)\n",
    "                       \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- For each gender, the age distribution is roughly similar and picks at around age of 60. This means that most customer are middle aged with some younger male customer at around age 25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1434,
   "metadata": {},
   "outputs": [
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   "source": [
    "def make_distribution_plot(title, hist, edges, x):\n",
    "    '''\n",
    "    This function \n",
    "        plots a density distribution of customer age\n",
    "    '''\n",
    "    \n",
    "    p = figure(title=title, tools='', background_fill_color=\"#fafafa\")\n",
    "    p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],\n",
    "           fill_color=\"navy\", line_color=\"white\", alpha=0.5)\n",
    "\n",
    "    p.y_range.start = 0\n",
    "    p.y_range.end = 0.03\n",
    "    p.legend.location = \"center_right\"\n",
    "    p.legend.background_fill_color = \"#fefefe\"\n",
    "    p.xaxis.axis_label = 'x'\n",
    "    p.yaxis.axis_label = 'Pr(x)'\n",
    "    p.grid.grid_line_color=\"white\"\n",
    "    \n",
    "    return p\n",
    "\n",
    "measured = np.asarray(age_df.age.tolist(), dtype=np.float32)\n",
    "hist, edges = np.histogram(measured, density=True, bins=84)\n",
    "x = measured\n",
    "\n",
    "output_file('age_distribution.html')\n",
    "p1 = make_distribution_plot(\"Age Distribution\", hist, edges, x)\n",
    "show(p1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's create a histogram showing income range against number of customers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Income in $')"
      ]
     },
     "execution_count": 1435,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = profile.income.hist(bins = 20)\n",
    "axs.grid(False)\n",
    "axs.set_ylabel('# of customers')\n",
    "axs.set_xlabel('Income in $')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- Surprisingly, there are many customers who falls into relatively lower income group and very few customers who falls into relatively high income groups. This could be due to the fact that younger customers may be earning less and younger customers are in general more technology oriented compared with older folks.\n",
    "\n",
    "**Next, let's create a chart showing customer sign up year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = profile.became_member_on.groupby(profile.became_member_on.dt.year).hist(bins=30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    " - The above chart shows that there is a steady increse in the number of customer who started using starbucks app. However, there is steep fall at the end of 2017. This could also be due to sampling bias (more customers have been sampled from mid 2017 duration) to prepare the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's draw some pair-wise plot of demographics data. We should drop the NA values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 588.85x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_pair_plot(df):\n",
    "    \n",
    "    sns.set(style= \"ticks\", color_codes=True)\n",
    "    normal_pair_plot = sns.pairplot(df[['age', \n",
    "                      'income', \n",
    "                      'gender', \n",
    "                      'member_since_x_days']].dropna(), \n",
    "             hue='gender', \n",
    "             palette=\"husl\", \n",
    "             markers=[\"o\", \"s\", \"D\"])\n",
    "    normal_pair_plot\n",
    "    \n",
    "draw_pair_plot(profile)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation: It seems that there are not many female customers and the female group is relatively older and has higher income**\n",
    "\n",
    "Next, Let's visualise a pair plot using linear regression fit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x12ad6e250>"
      ]
     },
     "execution_count": 1443,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 588.85x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fit linear regression models to the scatter plots:\n",
    "sns.set(style= \"ticks\", color_codes=True)\n",
    "reg_pair_plot = sns.pairplot(profile[['age', \n",
    "                      'income', \n",
    "                      'gender', \n",
    "                      'member_since_x_days']].dropna(), \n",
    "             hue='gender', \n",
    "             palette=\"PuOr\", \n",
    "             markers=[\"o\", \"s\", \"D\"], kind= \"reg\")\n",
    "reg_pair_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "The above set of chart leads to some interesting observations\n",
    "\n",
    "- There exist no customer in the age group [20, 30] having income greater thean 80K.\n",
    "- Similarly there exists not customer in the age group [40, 50] having income greater than 100K.\n",
    "- All the customer who became member more than 2000 days ago has income less than 100K. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C : Transcript "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>value</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 1.5899999999999999}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 9.53}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 3.61}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 3.5300000000000002}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 4.05}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person           event  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef  offer received   \n",
       "1       a03223e636434f42ac4c3df47e8bac43  offer received   \n",
       "2       e2127556f4f64592b11af22de27a7932  offer received   \n",
       "3       8ec6ce2a7e7949b1bf142def7d0e0586  offer received   \n",
       "4       68617ca6246f4fbc85e91a2a49552598  offer received   \n",
       "...                                  ...             ...   \n",
       "306529  b3a1272bc9904337b331bf348c3e8c17     transaction   \n",
       "306530  68213b08d99a4ae1b0dcb72aebd9aa35     transaction   \n",
       "306531  a00058cf10334a308c68e7631c529907     transaction   \n",
       "306532  76ddbd6576844afe811f1a3c0fbb5bec     transaction   \n",
       "306533  c02b10e8752c4d8e9b73f918558531f7     transaction   \n",
       "\n",
       "                                                   value  time  \n",
       "0       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}     0  \n",
       "1       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}     0  \n",
       "2       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}     0  \n",
       "3       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}     0  \n",
       "4       {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}     0  \n",
       "...                                                  ...   ...  \n",
       "306529                    {'amount': 1.5899999999999999}   714  \n",
       "306530                                  {'amount': 9.53}   714  \n",
       "306531                                  {'amount': 3.61}   714  \n",
       "306532                    {'amount': 3.5300000000000002}   714  \n",
       "306533                                  {'amount': 4.05}   714  \n",
       "\n",
       "[306534 rows x 4 columns]"
      ]
     },
     "execution_count": 1450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "The initial looks at transcript dataset suggests that value column is encoded as dictionary.\n",
    "If event is related to offers, then values column encode offer id and in case of transaction event, it encode the amount of transcation.\n",
    "\n",
    "Therefore, it makes sense to decode the value column into separate columns to better understand the relatationship between event status and corresponding effect on amount spent by the customers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1445,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's transform value column into separate columns\n",
    "\n",
    "def transform_value_column(transaction):\n",
    "    '''transform/unpivot the value column and return new transcript dataframe'''\n",
    "    \n",
    "    values = pd.DataFrame(transaction.value.tolist())\n",
    "    values.offer_id.update(values['offer id'])\n",
    "    values = values.drop('offer id', axis=1)\n",
    "    \n",
    "    return transaction.join(values).drop('value', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1451,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript = transform_value(transcript)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             person           event  time  amount  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0     NaN   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0     NaN   \n",
       "\n",
       "                           offer_id  reward  \n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9     NaN  \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7     NaN  "
      ]
     },
     "execution_count": 1452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction        138953\n",
       "offer received      76277\n",
       "offer viewed        57725\n",
       "offer completed     33579\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 1455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.event.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's create a pie chart to visulise how many offer were completed and viewed relative to offers received**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1453,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://docs.bokeh.org/en/latest/docs/gallery/pie_chart.html?highlight=pie%20chart (derived from here)\n",
    "\n",
    "def draw_pie_chart(data_dict, output_file_name):\n",
    "    \n",
    "    '''\n",
    "    This function\n",
    "     - draws a pie chart out of given data_dict \n",
    "     - output a html file with given 'output_file_name'\n",
    "    '''\n",
    "    \n",
    "    from math import pi\n",
    "    import pandas as pd\n",
    "    from bokeh.io import output_file, show\n",
    "    from bokeh.palettes import Category20c\n",
    "    from bokeh.plotting import figure\n",
    "    from bokeh.transform import cumsum\n",
    "\n",
    "    output_file(output_file_name + \".html\")\n",
    "    x = data_dict\n",
    "    data = pd.Series(x).reset_index(name='value').rename(columns={'index':'event'})\n",
    "    data['angle'] = data['value']/data['value'].sum() * 2*pi\n",
    "    data['color'] = Category20c[len(x)]\n",
    "\n",
    "    p = figure(plot_height=350, title=\"Pie Chart\", toolbar_location=None,\n",
    "           tools=\"hover\", tooltips=\"@event: @value\", x_range=(-0.5, 1.0))\n",
    "\n",
    "    p.wedge(x=0, y=1, radius=0.4,\n",
    "        start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),\n",
    "        line_color=\"white\", fill_color='color', legend_field='event', source=data)\n",
    "\n",
    "    p.axis.axis_label=None\n",
    "    p.axis.visible=False\n",
    "    p.grid.grid_line_color = None\n",
    "\n",
    "    show(p)"
   ]
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  {
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   "execution_count": 1454,
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       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1047833"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_dict_1 = {\n",
    "    'Transction': 138953,\n",
    "    'Offer received': 76277,\n",
    "    'Offer viewed': 57725,\n",
    "    'Offer completed': 33579\n",
    "}\n",
    "\n",
    "draw_pie_chart(data_dict_1, 'offer_distribution_1')\n",
    "\n",
    "data_dict_2 = {\n",
    "    'Offer received': 76277,\n",
    "    'Offer viewed': 57725,\n",
    "    'Offer completed': 33579\n",
    "}\n",
    "\n",
    "draw_pie_chart(data_dict_2, 'offer_distribution_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's do a quick analysis to check if there exist extra customers not found in the profile data set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17000"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_p_set =set(transcript['person'].values.tolist())\n",
    "\n",
    "len(t_p_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17000"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_set = set(profile['id'].values.tolist())\n",
    "\n",
    "len(p_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "print(t_p_set - p_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation* <br>\n",
    "The above analysis suggests that all the persons in transaction is found in the profile \n",
    "i.e. for each transaction, there is person with known demographics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person    0\n",
       "event     0\n",
       "value     0\n",
       "time      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "First of all, we should find out the total count of the completed offers after being viewed for each customer.\n",
    "The current data includes 'offers completed' by customers, knowingly or unknowingly.  The customers who have unknowingly completed offers should not be sent out further offers. This target group represents profitable customers for the company.\n",
    "\n",
    "In order to answer the above question,\n",
    "\n",
    "Let's find out customers who have received the offers but have not viewed it. We combine our transcript dataset with the customer demographics (profiles) first and then identify those customers.\n",
    "\n",
    "It could also be the case a single customer may have received multiple offers, but he may not view all of them but may have completed them unknowingly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5: Data Preprocessing & Advanced Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1459,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_data(transcript, profile, portfolio):\n",
    "    '''\n",
    "    This function\n",
    "    - joins a given dataframes in a single dataframe based on common columns\n",
    "    '''\n",
    "    \n",
    "    df = transcript.merge(profile, left_on='person', right_on='id',\n",
    "                                 how='left').drop('id', axis=1)\n",
    "    \n",
    "    df = df.merge(portfolio,\n",
    "        left_on='offer_id', right_on='id', how='left').drop('id', axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1460,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = join_data(transcript, profile, portfolio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1464,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 21 columns</p>\n",
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       "                             person           event  time  amount  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0     NaN   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0     NaN   \n",
       "2  e2127556f4f64592b11af22de27a7932  offer received     0     NaN   \n",
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       "4  68617ca6246f4fbc85e91a2a49552598  offer received     0     NaN   \n",
       "\n",
       "                           offer_id  reward_x gender  age became_member_on  \\\n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9       NaN      F   75       2017-05-09   \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7       NaN   None  118       2017-08-04   \n",
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       "4  4d5c57ea9a6940dd891ad53e9dbe8da0       NaN   None  118       2017-10-02   \n",
       "\n",
       "     income  ...  difficulty  reward_y  duration  is_mobile  is_web  \\\n",
       "0  100000.0  ...         5.0       5.0       7.0        1.0     1.0   \n",
       "1       NaN  ...        20.0       5.0      10.0        0.0     1.0   \n",
       "2   70000.0  ...        10.0       2.0       7.0        1.0     1.0   \n",
       "3       NaN  ...        10.0       2.0      10.0        1.0     1.0   \n",
       "4       NaN  ...        10.0      10.0       5.0        1.0     1.0   \n",
       "\n",
       "   is_social  is_email  bogo  discount  informational  \n",
       "0        0.0       1.0   1.0       0.0            0.0  \n",
       "1        0.0       1.0   0.0       1.0            0.0  \n",
       "2        0.0       1.0   0.0       1.0            0.0  \n",
       "3        1.0       1.0   0.0       1.0            0.0  \n",
       "4        1.0       1.0   1.0       0.0            0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 1464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['person', 'event', 'time', 'amount', 'offer_id', 'reward_x', 'gender',\n",
       "       'age', 'became_member_on', 'income', 'member_since_x_days',\n",
       "       'difficulty', 'reward_y', 'duration', 'is_mobile', 'is_web',\n",
       "       'is_social', 'is_email', 'bogo', 'discount', 'informational'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look at the column names\n",
    "merged_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1466,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's rename rewards columns to signify that _x -> _t (transcript) and _x -> _p (portfoli)\n",
    "merged_df = merged_df.rename(columns={'reward_x' : 'rewards_t' , 'reward_y' : 'rewards_p'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have joined dataset which we should explore further.\n",
    "\n",
    "As first step, let's query a datset to find all the associated events for two particular customers and display it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1467,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12650</th>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12651</th>\n",
       "      <td>d1ede868e29245ea91818a903fec04c6</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O</td>\n",
       "      <td>53</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12652</th>\n",
       "      <td>102e9454054946fda62242d2e176fdce</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>69</td>\n",
       "      <td>2016-08-14</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12653</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>be8a5d1981a2458d90b255ddc7e0d174</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>2014-05-27</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12654</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12657</th>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>34.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12659</th>\n",
       "      <td>54890f68699049c2a04d415abc25e717</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>13.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12670</th>\n",
       "      <td>b2f1cd155b864803ad8334cdf13c4bd2</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>19.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12671</th>\n",
       "      <td>fe97aa22dd3e48c8b143116a8403dd52</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>18.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>39</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12658</th>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>42</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12672</th>\n",
       "      <td>fe97aa22dd3e48c8b143116a8403dd52</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>39</td>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12679</th>\n",
       "      <td>629fc02d56414d91bca360decdfa9288</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>M</td>\n",
       "      <td>52</td>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12692</th>\n",
       "      <td>676506bad68e4161b9bbaffeb039626b</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M</td>\n",
       "      <td>37</td>\n",
       "      <td>2017-05-15</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12697</th>\n",
       "      <td>8f7dd3b2afe14c078eb4f6e6fe4ba97d</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M</td>\n",
       "      <td>48</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15561</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47582</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>transaction</td>\n",
       "      <td>132</td>\n",
       "      <td>19.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47583</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49502</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>transaction</td>\n",
       "      <td>144</td>\n",
       "      <td>17.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12653</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12654</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>transaction</td>\n",
       "      <td>0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15565</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>transaction</td>\n",
       "      <td>6</td>\n",
       "      <td>1.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18071</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>transaction</td>\n",
       "      <td>12</td>\n",
       "      <td>4.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person            event  time  amount  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef   offer received     0     NaN   \n",
       "1      a03223e636434f42ac4c3df47e8bac43   offer received     0     NaN   \n",
       "2      e2127556f4f64592b11af22de27a7932   offer received     0     NaN   \n",
       "3      8ec6ce2a7e7949b1bf142def7d0e0586   offer received     0     NaN   \n",
       "4      68617ca6246f4fbc85e91a2a49552598   offer received     0     NaN   \n",
       "12650  389bc3fa690240e798340f5a15918d5c     offer viewed     0     NaN   \n",
       "12651  d1ede868e29245ea91818a903fec04c6     offer viewed     0     NaN   \n",
       "12652  102e9454054946fda62242d2e176fdce     offer viewed     0     NaN   \n",
       "12653  02c083884c7d45b39cc68e1314fec56c     offer viewed     0     NaN   \n",
       "12655  be8a5d1981a2458d90b255ddc7e0d174     offer viewed     0     NaN   \n",
       "12654  02c083884c7d45b39cc68e1314fec56c      transaction     0    0.83   \n",
       "12657  9fa9ae8f57894cc9a3b8a9bbe0fc1b2f      transaction     0   34.56   \n",
       "12659  54890f68699049c2a04d415abc25e717      transaction     0   13.23   \n",
       "12670  b2f1cd155b864803ad8334cdf13c4bd2      transaction     0   19.51   \n",
       "12671  fe97aa22dd3e48c8b143116a8403dd52      transaction     0   18.97   \n",
       "12658  9fa9ae8f57894cc9a3b8a9bbe0fc1b2f  offer completed     0     NaN   \n",
       "12672  fe97aa22dd3e48c8b143116a8403dd52  offer completed     0     NaN   \n",
       "12679  629fc02d56414d91bca360decdfa9288  offer completed     0     NaN   \n",
       "12692  676506bad68e4161b9bbaffeb039626b  offer completed     0     NaN   \n",
       "12697  8f7dd3b2afe14c078eb4f6e6fe4ba97d  offer completed     0     NaN   \n",
       "0      78afa995795e4d85b5d9ceeca43f5fef   offer received     0     NaN   \n",
       "15561  78afa995795e4d85b5d9ceeca43f5fef     offer viewed     6     NaN   \n",
       "47582  78afa995795e4d85b5d9ceeca43f5fef      transaction   132   19.89   \n",
       "47583  78afa995795e4d85b5d9ceeca43f5fef  offer completed   132     NaN   \n",
       "49502  78afa995795e4d85b5d9ceeca43f5fef      transaction   144   17.78   \n",
       "27     02c083884c7d45b39cc68e1314fec56c   offer received     0     NaN   \n",
       "12653  02c083884c7d45b39cc68e1314fec56c     offer viewed     0     NaN   \n",
       "12654  02c083884c7d45b39cc68e1314fec56c      transaction     0    0.83   \n",
       "15565  02c083884c7d45b39cc68e1314fec56c      transaction     6    1.44   \n",
       "18071  02c083884c7d45b39cc68e1314fec56c      transaction    12    4.56   \n",
       "\n",
       "                               offer_id  rewards_t gender  age  \\\n",
       "0      9b98b8c7a33c4b65b9aebfe6a799e6d9        NaN      F   75   \n",
       "1      0b1e1539f2cc45b7b9fa7c272da2e1d7        NaN   None  118   \n",
       "2      2906b810c7d4411798c6938adc9daaa5        NaN      M   68   \n",
       "3      fafdcd668e3743c1bb461111dcafc2a4        NaN   None  118   \n",
       "4      4d5c57ea9a6940dd891ad53e9dbe8da0        NaN   None  118   \n",
       "12650  f19421c1d4aa40978ebb69ca19b0e20d        NaN      M   65   \n",
       "12651  5a8bc65990b245e5a138643cd4eb9837        NaN      O   53   \n",
       "12652  4d5c57ea9a6940dd891ad53e9dbe8da0        NaN      F   69   \n",
       "12653  ae264e3637204a6fb9bb56bc8210ddfd        NaN      F   20   \n",
       "12655  5a8bc65990b245e5a138643cd4eb9837        NaN      M   39   \n",
       "12654                               NaN        NaN      F   20   \n",
       "12657                               NaN        NaN      M   42   \n",
       "12659                               NaN        NaN      M   36   \n",
       "12670                               NaN        NaN      F   55   \n",
       "12671                               NaN        NaN      F   39   \n",
       "12658  2906b810c7d4411798c6938adc9daaa5        2.0      M   42   \n",
       "12672  fafdcd668e3743c1bb461111dcafc2a4        2.0      F   39   \n",
       "12679  9b98b8c7a33c4b65b9aebfe6a799e6d9        5.0      M   52   \n",
       "12692  ae264e3637204a6fb9bb56bc8210ddfd       10.0      M   37   \n",
       "12697  4d5c57ea9a6940dd891ad53e9dbe8da0       10.0      M   48   \n",
       "0      9b98b8c7a33c4b65b9aebfe6a799e6d9        NaN      F   75   \n",
       "15561  9b98b8c7a33c4b65b9aebfe6a799e6d9        NaN      F   75   \n",
       "47582                               NaN        NaN      F   75   \n",
       "47583  9b98b8c7a33c4b65b9aebfe6a799e6d9        5.0      F   75   \n",
       "49502                               NaN        NaN      F   75   \n",
       "27     ae264e3637204a6fb9bb56bc8210ddfd        NaN      F   20   \n",
       "12653  ae264e3637204a6fb9bb56bc8210ddfd        NaN      F   20   \n",
       "12654                               NaN        NaN      F   20   \n",
       "15565                               NaN        NaN      F   20   \n",
       "18071                               NaN        NaN      F   20   \n",
       "\n",
       "      became_member_on    income  ...  difficulty  rewards_p  duration  \\\n",
       "0           2017-05-09  100000.0  ...         5.0        5.0       7.0   \n",
       "1           2017-08-04       NaN  ...        20.0        5.0      10.0   \n",
       "2           2018-04-26   70000.0  ...        10.0        2.0       7.0   \n",
       "3           2017-09-25       NaN  ...        10.0        2.0      10.0   \n",
       "4           2017-10-02       NaN  ...        10.0       10.0       5.0   \n",
       "12650       2018-02-09   53000.0  ...         5.0        5.0       5.0   \n",
       "12651       2017-09-16   52000.0  ...         0.0        0.0       3.0   \n",
       "12652       2016-08-14   57000.0  ...        10.0       10.0       5.0   \n",
       "12653       2016-07-11   30000.0  ...        10.0       10.0       7.0   \n",
       "12655       2014-05-27   51000.0  ...         0.0        0.0       3.0   \n",
       "12654       2016-07-11   30000.0  ...         NaN        NaN       NaN   \n",
       "12657       2016-01-17   96000.0  ...         NaN        NaN       NaN   \n",
       "12659       2017-12-28   56000.0  ...         NaN        NaN       NaN   \n",
       "12670       2017-10-16   94000.0  ...         NaN        NaN       NaN   \n",
       "12671       2017-12-17   67000.0  ...         NaN        NaN       NaN   \n",
       "12658       2016-01-17   96000.0  ...        10.0        2.0       7.0   \n",
       "12672       2017-12-17   67000.0  ...        10.0        2.0      10.0   \n",
       "12679       2018-06-05   72000.0  ...         5.0        5.0       7.0   \n",
       "12692       2017-05-15   92000.0  ...        10.0       10.0       7.0   \n",
       "12697       2015-09-03   62000.0  ...        10.0       10.0       5.0   \n",
       "0           2017-05-09  100000.0  ...         5.0        5.0       7.0   \n",
       "15561       2017-05-09  100000.0  ...         5.0        5.0       7.0   \n",
       "47582       2017-05-09  100000.0  ...         NaN        NaN       NaN   \n",
       "47583       2017-05-09  100000.0  ...         5.0        5.0       7.0   \n",
       "49502       2017-05-09  100000.0  ...         NaN        NaN       NaN   \n",
       "27          2016-07-11   30000.0  ...        10.0       10.0       7.0   \n",
       "12653       2016-07-11   30000.0  ...        10.0       10.0       7.0   \n",
       "12654       2016-07-11   30000.0  ...         NaN        NaN       NaN   \n",
       "15565       2016-07-11   30000.0  ...         NaN        NaN       NaN   \n",
       "18071       2016-07-11   30000.0  ...         NaN        NaN       NaN   \n",
       "\n",
       "       is_mobile  is_web  is_social  is_email  bogo  discount  informational  \n",
       "0            1.0     1.0        0.0       1.0   1.0       0.0            0.0  \n",
       "1            0.0     1.0        0.0       1.0   0.0       1.0            0.0  \n",
       "2            1.0     1.0        0.0       1.0   0.0       1.0            0.0  \n",
       "3            1.0     1.0        1.0       1.0   0.0       1.0            0.0  \n",
       "4            1.0     1.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12650        1.0     1.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12651        1.0     0.0        1.0       1.0   0.0       0.0            1.0  \n",
       "12652        1.0     1.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12653        1.0     0.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12655        1.0     0.0        1.0       1.0   0.0       0.0            1.0  \n",
       "12654        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "12657        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "12659        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "12670        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "12671        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "12658        1.0     1.0        0.0       1.0   0.0       1.0            0.0  \n",
       "12672        1.0     1.0        1.0       1.0   0.0       1.0            0.0  \n",
       "12679        1.0     1.0        0.0       1.0   1.0       0.0            0.0  \n",
       "12692        1.0     0.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12697        1.0     1.0        1.0       1.0   1.0       0.0            0.0  \n",
       "0            1.0     1.0        0.0       1.0   1.0       0.0            0.0  \n",
       "15561        1.0     1.0        0.0       1.0   1.0       0.0            0.0  \n",
       "47582        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "47583        1.0     1.0        0.0       1.0   1.0       0.0            0.0  \n",
       "49502        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "27           1.0     0.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12653        1.0     0.0        1.0       1.0   1.0       0.0            0.0  \n",
       "12654        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "15565        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "18071        NaN     NaN        NaN       NaN   NaN       NaN            NaN  \n",
       "\n",
       "[30 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.DataFrame().append([\n",
    "    merged_df.query('event==\"offer received\"').head(),\n",
    "    merged_df.query('event==\"offer viewed\"').head(),\n",
    "    merged_df.query('event==\"transaction\"').head(),\n",
    "    merged_df.query('event==\"offer completed\"').head(),\n",
    "    merged_df.query('person==\"78afa995795e4d85b5d9ceeca43f5fef\"').head(),\n",
    "    merged_df.query('person==\"02c083884c7d45b39cc68e1314fec56c\"').head()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "\n",
    "- As soon as an offer completes, an entry into 'rewards_t'  equal to the 'rewards_p' happens.\n",
    "\n",
    "- Timestamp started at 0 when the first offer received event occurred.\n",
    "\n",
    "- When a customer makes a transaction which is higher than the offered reward within the offer duration (duration column, expressed in days), offer completes immediately (see records 47582, 47583), and reward gets credited to the customer.\n",
    "\n",
    "- The customer may receive two different offers at the same time and may complete two offers at the same time given a transaction exceeding the rewards is made.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine events separately\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>rewards_t</th>\n",
       "      <th>rewards_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             person           event  time  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0   \n",
       "2  e2127556f4f64592b11af22de27a7932  offer received     0   \n",
       "3  8ec6ce2a7e7949b1bf142def7d0e0586  offer received     0   \n",
       "4  68617ca6246f4fbc85e91a2a49552598  offer received     0   \n",
       "\n",
       "                           offer_id  amount  duration  rewards_t  rewards_p  \n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9     NaN       7.0        NaN        5.0  \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7     NaN      10.0        NaN        5.0  \n",
       "2  2906b810c7d4411798c6938adc9daaa5     NaN       7.0        NaN        2.0  \n",
       "3  fafdcd668e3743c1bb461111dcafc2a4     NaN      10.0        NaN        2.0  \n",
       "4  4d5c57ea9a6940dd891ad53e9dbe8da0     NaN       5.0        NaN       10.0  "
      ]
     },
     "execution_count": 1469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_received_events = pd.DataFrame().append([\n",
    "    merged_df[['person', 'event', 'time', 'offer_id', 'amount', 'duration','rewards_t', 'rewards_p']]\n",
    "    .query('event==\"offer received\"')])\n",
    "\n",
    "offer_received_events.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 1470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_received_events.time.max()/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76277.000000\n",
       "mean       332.579519\n",
       "std        196.489548\n",
       "min          0.000000\n",
       "25%        168.000000\n",
       "50%        408.000000\n",
       "75%        504.000000\n",
       "max        576.000000\n",
       "Name: time, dtype: float64"
      ]
     },
     "execution_count": 1471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_received_events.time.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      12650\n",
       "168    12669\n",
       "504    12704\n",
       "336    12711\n",
       "576    12765\n",
       "408    12778\n",
       "Name: time, dtype: int64"
      ]
     },
     "execution_count": 1472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_received_events.time.value_counts().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_sent = offer_received_events.time.value_counts().sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 168, 336, 408, 504, 576]\n",
      "[0.0, 7.0, 14.0, 17.0, 21.0, 24.0]\n"
     ]
    }
   ],
   "source": [
    "#Let's find out on which days offers are sent by dividing timeline by 24.\n",
    "\n",
    "offer_sent_time_in_hours = offer_received_events.time.value_counts().sort_index(ascending=True).index.tolist()\n",
    "\n",
    "offer_sent_time_in_days = []\n",
    "\n",
    "for x in offer_sent_time_in_hours:\n",
    "    if x != 0:\n",
    "        x = x/24\n",
    "        offer_sent_time_in_days.append(x)\n",
    "        \n",
    "offer_sent_time_in_days.append(0.0)\n",
    "\n",
    "print(offer_sent_time_in_hours)\n",
    "\n",
    "offer_sent_time_in_days.sort()\n",
    "print(offer_sent_time_in_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        6.000000\n",
       "mean     12712.833333\n",
       "std         50.830765\n",
       "min      12650.000000\n",
       "25%      12677.750000\n",
       "50%      12707.500000\n",
       "75%      12751.500000\n",
       "max      12778.000000\n",
       "Name: time, dtype: float64"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_received_events.time.value_counts().sort_values(ascending=True).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- We can see that the timeline range is [0, 576] for the offer received event. Every offer gets received in 24 days period (or the data is sampled only for 24 days).\n",
    "\n",
    "- Ther are only 6 distinct time values present, and if we divide those values by 24, we get on which day the offer has been sent.\n",
    "\n",
    "- If we find statistics of those 6 days, we can see that on each of these days, around 12712 offers get received.\n",
    "\n",
    "- Assuming the offer gets received in equal proportions on each instance, the dataset is balanced with respect to the number of offers sent out at each time interval.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore offers viewed events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1475,
   "metadata": {},
   "outputs": [],
   "source": [
    "offer_viewed_events = pd.DataFrame().append([\n",
    "    merged_df[['person', 'event', 'time', 'offer_id', 'amount', 'duration','rewards_t', 'rewards_p']]\n",
    "    .query('event==\"offer viewed\"')])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>duration</th>\n",
       "      <th>rewards_t</th>\n",
       "      <th>rewards_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12650</th>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12651</th>\n",
       "      <td>d1ede868e29245ea91818a903fec04c6</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12652</th>\n",
       "      <td>102e9454054946fda62242d2e176fdce</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12653</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12655</th>\n",
       "      <td>be8a5d1981a2458d90b255ddc7e0d174</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>0</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person         event  time  \\\n",
       "12650  389bc3fa690240e798340f5a15918d5c  offer viewed     0   \n",
       "12651  d1ede868e29245ea91818a903fec04c6  offer viewed     0   \n",
       "12652  102e9454054946fda62242d2e176fdce  offer viewed     0   \n",
       "12653  02c083884c7d45b39cc68e1314fec56c  offer viewed     0   \n",
       "12655  be8a5d1981a2458d90b255ddc7e0d174  offer viewed     0   \n",
       "\n",
       "                               offer_id  amount  duration  rewards_t  \\\n",
       "12650  f19421c1d4aa40978ebb69ca19b0e20d     NaN       5.0        NaN   \n",
       "12651  5a8bc65990b245e5a138643cd4eb9837     NaN       3.0        NaN   \n",
       "12652  4d5c57ea9a6940dd891ad53e9dbe8da0     NaN       5.0        NaN   \n",
       "12653  ae264e3637204a6fb9bb56bc8210ddfd     NaN       7.0        NaN   \n",
       "12655  5a8bc65990b245e5a138643cd4eb9837     NaN       3.0        NaN   \n",
       "\n",
       "       rewards_p  \n",
       "12650        5.0  \n",
       "12651        0.0  \n",
       "12652       10.0  \n",
       "12653       10.0  \n",
       "12655        0.0  "
      ]
     },
     "execution_count": 1476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_viewed_events.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    57725.000000\n",
       "mean       354.290515\n",
       "std        199.317684\n",
       "min          0.000000\n",
       "25%        180.000000\n",
       "50%        408.000000\n",
       "75%        516.000000\n",
       "max        714.000000\n",
       "Name: time, dtype: float64"
      ]
     },
     "execution_count": 1477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_viewed_events.time.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1478,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_duration_in_days = offer_viewed_events.duration.value_counts().index.max()\n",
    "max_duration_in_hours = max_duration_in_days * 24\n",
    "max_allowed_view_duration = max_duration_in_hours + 576 # 576 is the last timestamp on which offers were sent out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "816.0"
      ]
     },
     "execution_count": 1479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_allowed_view_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1480,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All viewed offers event has time stamp value less than or equal to max allowed duration to view offers\n"
     ]
    }
   ],
   "source": [
    "if offer_viewed_events.time.max() <= max_allowed_view_duration:\n",
    "    print('All viewed offers event has time stamp value less than or equal to max allowed duration to view offers')\n",
    "else:\n",
    "    print('Time stamp for viewed offers event is not marked properly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408    2210\n",
       "504    2153\n",
       "576    2152\n",
       "168    2120\n",
       "336    2103\n",
       "       ... \n",
       "318      42\n",
       "156      39\n",
       "324      35\n",
       "330      28\n",
       "162      25\n",
       "Name: time, Length: 120, dtype: int64"
      ]
     },
     "execution_count": 1481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_viewed_events.time.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above series suggest that there are some higher number of offers being viewed at particular time.\n",
    "Let's plot a chart of number of offers viewed against a timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1484,
   "metadata": {},
   "outputs": [],
   "source": [
    "offer_viewed_time = offer_viewed_events.time.value_counts().sort_index()\n",
    "offer_sent = offer_received_events.time.value_counts().sort_index()\n",
    "\n",
    "#we will shrink the count of offers sent by 75% to make the chart looks nice.See legent for counting actual values\n",
    "offer_sent_shrinked = offer_sent/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"ecfd94c3-e945-4380-a9b0-c2149d87ac39\" data-root-id=\"1058190\"></div>\n"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"e84e67e2-4297-49ce-a052-7339b6932659\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1058201\"}],\"center\":[{\"id\":\"1058204\"},{\"id\":\"1058208\"},{\"id\":\"1058240\"}],\"left\":[{\"id\":\"1058205\"}],\"plot_width\":900,\"renderers\":[{\"id\":\"1058226\"},{\"id\":\"1058231\"},{\"id\":\"1058245\"}],\"title\":{\"id\":\"1058191\"},\"toolbar\":{\"id\":\"1058215\"},\"x_range\":{\"id\":\"1058193\"},\"x_scale\":{\"id\":\"1058197\"},\"y_range\":{\"id\":\"1058195\"},\"y_scale\":{\"id\":\"1058199\"}},\"id\":\"1058190\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"1058242\"}},\"id\":\"1058246\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"# of offer viewed over time (in 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   "source": [
    "p1 = None\n",
    "\n",
    "output_file('offers_viewed_timeline.html')\n",
    "\n",
    "p1 = figure(title=\"# of offer viewed over time (in hours)\", \n",
    "            x_axis_label='timestamp in hours', \n",
    "            y_axis_label = '# of events',\n",
    "            plot_width=900, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 50, 100, 200, 500, 1000, 2000, 3000, 3500])\n",
    "p1.yaxis.ticker = yticks\n",
    "\n",
    "\n",
    "p1.line(offer_viewed_time.index, offer_viewed_time, line_width =2)\n",
    "p1.circle(offer_viewed_time.index, offer_viewed_time, fill_color = \"white\",color = \"navy\", size=8 , legend_label='offer_viewed')\n",
    "p1.cross(offer_sent_shrinked.index, offer_sent_shrinked, size=12,\n",
    "           color=\"#E6550D\", line_width=2, legend_label='* 4 = offers_sent')\n",
    "\n",
    "show(p1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- The above chart displays the number of offers viewed against timestamp in hours. There are six peaks where the number of viewed offers increases dramatically. \n",
    "\n",
    "- I have embedded the number of offers received (correct received offer count is 4 times the marked one) in the chart. When new offers get received, the count of viewed offer increases and then slowly decreases over time. \n",
    "\n",
    "- Generally speaking, most offers are viewed within 24 hours of receipt.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyze offers completed events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1486,
   "metadata": {},
   "outputs": [],
   "source": [
    "offer_completed_events = pd.DataFrame().append([\n",
    "    merged_df[['person', 'event', 'time', 'offer_id', 'amount', 'duration','rewards_t', 'rewards_p']]\n",
    "    .query('event==\"offer completed\"')])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>duration</th>\n",
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       "      <th>12658</th>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12672</th>\n",
       "      <td>fe97aa22dd3e48c8b143116a8403dd52</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                 person            event  time  \\\n",
       "12658  9fa9ae8f57894cc9a3b8a9bbe0fc1b2f  offer completed     0   \n",
       "12672  fe97aa22dd3e48c8b143116a8403dd52  offer completed     0   \n",
       "\n",
       "                               offer_id  amount  duration  rewards_t  \\\n",
       "12658  2906b810c7d4411798c6938adc9daaa5     NaN       7.0        2.0   \n",
       "12672  fafdcd668e3743c1bb461111dcafc2a4     NaN      10.0        2.0   \n",
       "\n",
       "       rewards_p  \n",
       "12658        2.0  \n",
       "12672        2.0  "
      ]
     },
     "execution_count": 1487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offer_completed_events.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1488,
   "metadata": {},
   "outputs": [],
   "source": [
    "offer_completed = offer_completed_events.time.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1490,
   "metadata": {},
   "outputs": [
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
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     "output_type": "display_data"
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   "source": [
    "p2 = None\n",
    "output_file(\"offers_completed_timeline.html\")\n",
    "p2 = figure(title=\"# of offer completed over time (in hours)\", \n",
    "            x_axis_label='timestamp in hours', \n",
    "            y_axis_label = '# of event',\n",
    "            plot_width=900, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 100, 200, 300, 400, 600, 800])\n",
    "p2.yaxis.ticker = yticks\n",
    "\n",
    "\n",
    "p2.line(offer_completed.index, offer_completed, line_width =2 , color= 'navy')\n",
    "p2.square(offer_completed.index, offer_completed, color = \"olive\", size=8 , legend_label='offer_completed')\n",
    "\n",
    "show(p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- The above chart shows the number of completed offers against a timestamp in hours. It follows a similar pattern as the graph before because new offers are sent 6 times in 24 day period, and hence the chart has 6 peaks. \n",
    "\n",
    "- Between each peak, the completed offer chart decreases gradually, just like viewed offers chart. However, the decrease is not as smooth as for the viewed offer. There are some local picks during the decrease.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyse transaction event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_events = merged_df.loc[merged_df['event'].isin(['transaction'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1493,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1062.28"
      ]
     },
     "execution_count": 1493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_events.amount.max() #highest value single transaction recorded in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions_count = transaction_events.time.value_counts()\n",
    "transactions_count_index_sorted = transaction_events.time.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1492,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "633"
      ]
     },
     "execution_count": 1492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_count.min() #minimum number of transaction at any given point of time in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1494,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "\n",
       "\n",
       "\n",
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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     "output_type": "display_data"
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   ],
   "source": [
    "output_file(\"transaction_timeline.html\")\n",
    "p3 = figure(title=\"# of transacations over time (in hours)\", \n",
    "            x_axis_label='timestamp in hours', \n",
    "            y_axis_label = '# of events',\n",
    "            plot_width=900, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 50, 100, 200, 300, 400, 500, 600, 800, 900, 1100, 1300, 1700, 2200, 3000])\n",
    "p3.yaxis.ticker = yticks\n",
    "\n",
    "p3.line(transactions_count_index_sorted.index, transactions_count_index_sorted, line_width =2 , color= 'navy')\n",
    "p3.diamond_cross(transactions_count_index_sorted.index, transactions_count_index_sorted, color = \"red\", size=8 , legend_label='Transactions')\n",
    "\n",
    "show(p3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- The above chart shows that there are some initial transactions at timestamp 0.(635 transactions).\n",
    "\n",
    "- Now, some of it may have contributed to the completion of offers. For this, we should embed the completed offers chart to get some visual pictures.\n",
    "\n",
    "- The above chart seems to follow the picks as well, but it is not as smooth as other charts like offers viewed and offers completed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1495,
   "metadata": {},
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   "source": [
    "p4 = None\n",
    "output_file('offer_completed_tnx.html')\n",
    "p4 = figure(title=\"# of transacations over time (in hours)\", \n",
    "            x_axis_label='timestamp in hours', \n",
    "            y_axis_label = '# of events',\n",
    "            plot_width=950, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 50, 100, 200, 300, 400, 500, 600, 800, 900, 1100, 1300, 1700, 2200, 3000])\n",
    "p4.yaxis.ticker = yticks\n",
    "\n",
    "p4.line(transactions_count_index_sorted.index, transactions_count_index_sorted, line_width =2 , color= 'navy')\n",
    "p4.diamond_cross(transactions_count_index_sorted.index, transactions_count_index_sorted, color = \"red\", size=8 , legend_label='Transactions')\n",
    "\n",
    "\n",
    "#p3.line(s.index, s, line_width =2)\n",
    "#p3.circle(s.index, s, fill_color = \"white\",color = \"navy\", size=8 , legend_label='offer_viewed')\n",
    "p4.cross(s_sent_shrinked.index, s_sent_shrinked/2, size=12,\n",
    "           color=\"#E6550D\", line_width=2, legend_label='* 8 = offers_sent')\n",
    "\n",
    "\n",
    "p4.line(s_completed.index, s_completed, line_width =2 , color= 'navy')\n",
    "p4.square(s_completed.index, s_completed, color = \"olive\", size=8 , legend_label='offer_completed')\n",
    "\n",
    "show(p4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    " \n",
    "- There is a local peak between each offer_sent our interval for transactions. This implies that when offers are received, customers quickly do some transactions resulting in offer completion.\n",
    "\n",
    "- At time stamp 0, there exist around 600 transactions. Around 200 of them lead to offer completion (number of offers completed at timestamp 0), which is roughly 25% conversion rate. This figure  is roughly in agreement with the ratio of the total number of completed offers to the total number of transactions => 138953/33579)*100 = 24.16%\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138953"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_count_index_sorted.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33579"
      ]
     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_completed.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.16572510129324"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_rate = s_completed.sum()/transactions_count_index_sorted.sum()\n",
    "conversion_rate*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Next Steps*\n",
    "\n",
    "- There exist some high-value transactions (above 100 USD). It could be some large orders from individuals who are organizing events or from corporate customers. Such a high-value transaction leads to all of the current offers associated with those customers to completion. But in reality, these transactions were not motivated to complete the offers, and therefore it should be considered as side effects. Sending out offers to such high paying customers would not lead to an increase or decrease in their purchasing patterns. Therefore we should remove such high-value transactions from the dataset.\n",
    "\n",
    "- Next, we should also find out and remove non-responsive customers. I define non-responsive customer  = no received offered viewed + not a single transaction made \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_by_person = merged_df.groupby('person')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_no_transaction =merged_df_by_person.count().query('amount == 0').index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = set(customers_no_transaction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Future Task: Get profile of above 422 customers and anaylze their demographics if those are interested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "metadata": {},
   "outputs": [],
   "source": [
    "offers_viewed = merged_df.query \\\n",
    " ('person in @customers_no_transaction and event == \"offer viewed\"').groupby('person').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = set(offers_viewed.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = A-B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'df9fc9a86ca84ef5aedde8925d5838ba', 'e63e42480aae4ede9f07cac49c8c3f78', 'f67a6524092d48a788a415c453bd2e00', '76341c0cc6684b3eb23661e195dfc9a3', 'ce1579c557c14f7785869dc80638bc0f', '83abd8407034461782483fb32d3d5f5c', 'af63cf0ed6ad4c458a03cb321927b463', 'afd41b230f924f9ca8f5ed6249616114', '57760e0d402d41ea970481bd848d1f51', '3a4e53046c544134bb1e7782248631d1'}\n"
     ]
    }
   ],
   "source": [
    "C\n",
    "print (C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 truly non responsive customers found\n"
     ]
    }
   ],
   "source": [
    "print(str(len(C)) + ' truly non responsive customers found')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "- Set C contains all the customer who have not viewed any offers and has not made any transaction. \n",
    "\n",
    "- Therfore Set C represents a truly non-responsive customers (should not be considered for further offers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [],
   "source": [
    "offers_received = merged_df.query \\\n",
    " ('person in @customers_no_transaction and event == \"offer received\"').groupby('person').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = set(offers_received.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "422"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = A-D\n",
    "len(E)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- There are 422 customer having made no transcation. Out of them, 412 has received and viewed offers while 10 of them has received and not viewed any offers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine a transaction amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1497,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions_amount = transaction_events.amount.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1498,
   "metadata": {},
   "outputs": [
    {
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of transactions\",\"formatter\":{\"id\":\"1068725\"},\"ticker\":{\"id\":\"1066311\"}},\"id\":\"1066295\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1066283\",\"type\":\"DataRange1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1066299\"},{\"id\":\"1066300\"},{\"id\":\"1066301\"},{\"id\":\"1066302\"},{\"id\":\"1066303\"}]},\"id\":\"1066305\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1068727\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1066292\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1066300\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1066287\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1066299\",\"type\":\"PanTool\"},{\"attributes\":{\"ticks\":[0,50,100,200,300,400,500]},\"id\":\"1066311\",\"type\":\"FixedTicker\"},{\"attributes\":{\"source\":{\"id\":\"1066313\"}},\"id\":\"1066317\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"# of transacations against spent amount)\"},\"id\":\"1066281\",\"type\":\"Title\"}],\"root_ids\":[\"1066280\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.1\"}};\n",
       "  var render_items = [{\"docid\":\"cc4453c5-05e0-49d4-9e53-bd77c7144ddd\",\"root_ids\":[\"1066280\"],\"roots\":{\"1066280\":\"38b184cd-44ab-4a2c-80fb-dca88b7627da\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1066280"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p5 = None\n",
    "output_file('transaction_amount.html')\n",
    "p5 = figure(title=\"# of transacations against spent amount)\", \n",
    "            x_axis_label='Amount($)', \n",
    "            y_axis_label = '# of transactions',\n",
    "            plot_width=900, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 50, 100, 200, 300, 400, 500])\n",
    "p5.yaxis.ticker = yticks\n",
    "\n",
    "p5.scatter(transactions_amount.index, transactions_amount, marker= \"circle\", color= 'orange', radius = 4)\n",
    "show(p5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "\n",
    "- There are some transactions > 1000 USD. \n",
    "\n",
    "- There are many more transactions in the range [0.05,50] than the rest of the range\n",
    "\n",
    "- Any transaction greater than 50 USD can be treated as a high-value transaction and not necessarily motivated to complete the offers.\n",
    "\n",
    "- We should remove any transaction > 50 in our data modeling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print some interesting information regarding transaction amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of repeated customers with more than 1 transaction with a value >= $50,00: 19\n",
      "Number of customers with transactions with a value >= $50,00: 687\n",
      "Number of customers with transactions with a value <= $50,00: 16577\n",
      "Number of customers with transactions with a value <= $10,00: 10975\n",
      "Number of customers with transactions with a value <= $5    : 8167\n",
      "Number of customers with transactions with a value <= $1    : 5293\n",
      "Number of customers with transactions with a value <= $0.25 : 1851\n"
     ]
    }
   ],
   "source": [
    "print('Number of repeated customers with more than 1 transaction with a value >= $50,00:',\n",
    "       transaction_events[transaction_events.amount >= 50].person.duplicated().sum())\n",
    "print('Number of customers with transactions with a value >= $50,00:',\n",
    "       transaction_events[transaction_events.amount >= 50].person.nunique())\n",
    "print('Number of customers with transactions with a value <= $50,00:',\n",
    "       transaction_events[transaction_events.amount <= 50].person.nunique())\n",
    "print('Number of customers with transactions with a value <= $10,00:',\n",
    "       transaction_events[transaction_events.amount <= 10].person.nunique())\n",
    "print('Number of customers with transactions with a value <= $5    :',\n",
    "       transaction_events[transaction_events.amount <= 5].person.nunique())\n",
    "print('Number of customers with transactions with a value <= $1    :',\n",
    "       transaction_events[transaction_events.amount <= 1].person.nunique())\n",
    "print('Number of customers with transactions with a value <= $0.25 :',\n",
    "       transaction_events[transaction_events.amount <= 0.25].person.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1501,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_df = merged_df\n",
    "#feat_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "422"
      ]
     },
     "execution_count": 838,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(A)\n",
    "#list(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1502,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>rewards_t</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "      <th>...</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>rewards_p</th>\n",
       "      <th>duration</th>\n",
       "      <th>is_mobile</th>\n",
       "      <th>is_web</th>\n",
       "      <th>is_social</th>\n",
       "      <th>is_email</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
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       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
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       "      <td>75</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             person           event  time  amount  \\\n",
       "0  78afa995795e4d85b5d9ceeca43f5fef  offer received     0     NaN   \n",
       "1  a03223e636434f42ac4c3df47e8bac43  offer received     0     NaN   \n",
       "\n",
       "                           offer_id  rewards_t gender  age became_member_on  \\\n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9        NaN      F   75       2017-05-09   \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7        NaN   None  118       2017-08-04   \n",
       "\n",
       "     income  ...  difficulty  rewards_p  duration  is_mobile  is_web  \\\n",
       "0  100000.0  ...         5.0        5.0       7.0        1.0     1.0   \n",
       "1       NaN  ...        20.0        5.0      10.0        0.0     1.0   \n",
       "\n",
       "   is_social  is_email  bogo  discount  informational  \n",
       "0        0.0       1.0   1.0       0.0            0.0  \n",
       "1        0.0       1.0   0.0       1.0            0.0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 1502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's remove non-responsive customers\n",
    "final_df = feat_df[~feat_df.person.isin(A)]\n",
    "final_df.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1503,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's remove transcation more than 50 USD. We need to fill NA values before we can filter those transactions\n",
    "final_df = final_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1504,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[final_df.amount <50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1505,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.reset_index(drop= True)\n",
    "# Adding cumulative amount spent\n",
    "final_df['cum_amount'] = final_df.groupby('person').amount.cumsum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will first understand the customer interaction with offers through some advanced visulisations.\n",
    "\n",
    "Based on that, we will define some custom calculated features that could be useful for our classification problem.\n",
    "\n",
    "Furthermore, the analysis should also help us in defining our label or precisely as part of Label Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1506,
   "metadata": {},
   "outputs": [],
   "source": [
    "def person_data(df, person):\n",
    "    '''\n",
    "    Displays unique customer's event history\n",
    "    \n",
    "    Parameters\n",
    "    -----------\n",
    "    person: if int then customer index as per the order in which \n",
    "    customer appears in transcript data, if string then person \n",
    "    referenced by their unique 'person' id           \n",
    "    '''\n",
    "    if type(person) == str:\n",
    "        return df[df.person == person]\n",
    "    else:\n",
    "        return df[df.person == df.person.unique()[person]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1507,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>offer_id</th>\n",
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       "      <th>gender</th>\n",
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       "      <th>...</th>\n",
       "      <th>rewards_p</th>\n",
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       "      <th>is_mobile</th>\n",
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       "      <th>is_social</th>\n",
       "      <th>is_email</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>cum_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>transaction</td>\n",
       "      <td>414</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>83</td>\n",
       "      <td>2018-01-27</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person           event  time  amount  \\\n",
       "89      4c29d22467af4d7faa137c4eedd65340  offer received     0    0.00   \n",
       "17664   4c29d22467af4d7faa137c4eedd65340     transaction    12    3.75   \n",
       "21835   4c29d22467af4d7faa137c4eedd65340    offer viewed    24    0.00   \n",
       "23730   4c29d22467af4d7faa137c4eedd65340     transaction    30    1.33   \n",
       "45937   4c29d22467af4d7faa137c4eedd65340     transaction   126    6.96   \n",
       "51564   4c29d22467af4d7faa137c4eedd65340     transaction   162    1.36   \n",
       "109489  4c29d22467af4d7faa137c4eedd65340  offer received   336    0.00   \n",
       "137605  4c29d22467af4d7faa137c4eedd65340    offer viewed   372    0.00   \n",
       "148647  4c29d22467af4d7faa137c4eedd65340  offer received   408    0.00   \n",
       "165231  4c29d22467af4d7faa137c4eedd65340    offer viewed   414    0.00   \n",
       "165232  4c29d22467af4d7faa137c4eedd65340     transaction   414    3.37   \n",
       "206982  4c29d22467af4d7faa137c4eedd65340  offer received   504    0.00   \n",
       "215350  4c29d22467af4d7faa137c4eedd65340    offer viewed   510    0.00   \n",
       "249912  4c29d22467af4d7faa137c4eedd65340  offer received   576    0.00   \n",
       "258459  4c29d22467af4d7faa137c4eedd65340    offer viewed   582    0.00   \n",
       "\n",
       "                                offer_id  rewards_t gender  age  \\\n",
       "89      5a8bc65990b245e5a138643cd4eb9837        0.0      M   83   \n",
       "17664                                  0        0.0      M   83   \n",
       "21835   5a8bc65990b245e5a138643cd4eb9837        0.0      M   83   \n",
       "23730                                  0        0.0      M   83   \n",
       "45937                                  0        0.0      M   83   \n",
       "51564                                  0        0.0      M   83   \n",
       "109489  f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   83   \n",
       "137605  f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   83   \n",
       "148647  ae264e3637204a6fb9bb56bc8210ddfd        0.0      M   83   \n",
       "165231  ae264e3637204a6fb9bb56bc8210ddfd        0.0      M   83   \n",
       "165232                                 0        0.0      M   83   \n",
       "206982  f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   83   \n",
       "215350  f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   83   \n",
       "249912  2298d6c36e964ae4a3e7e9706d1fb8c2        0.0      M   83   \n",
       "258459  2298d6c36e964ae4a3e7e9706d1fb8c2        0.0      M   83   \n",
       "\n",
       "       became_member_on   income  ...  rewards_p  duration  is_mobile  is_web  \\\n",
       "89           2018-01-27  46000.0  ...        0.0       3.0        1.0     0.0   \n",
       "17664        2018-01-27  46000.0  ...        0.0       0.0        0.0     0.0   \n",
       "21835        2018-01-27  46000.0  ...        0.0       3.0        1.0     0.0   \n",
       "23730        2018-01-27  46000.0  ...        0.0       0.0        0.0     0.0   \n",
       "45937        2018-01-27  46000.0  ...        0.0       0.0        0.0     0.0   \n",
       "51564        2018-01-27  46000.0  ...        0.0       0.0        0.0     0.0   \n",
       "109489       2018-01-27  46000.0  ...        5.0       5.0        1.0     1.0   \n",
       "137605       2018-01-27  46000.0  ...        5.0       5.0        1.0     1.0   \n",
       "148647       2018-01-27  46000.0  ...       10.0       7.0        1.0     0.0   \n",
       "165231       2018-01-27  46000.0  ...       10.0       7.0        1.0     0.0   \n",
       "165232       2018-01-27  46000.0  ...        0.0       0.0        0.0     0.0   \n",
       "206982       2018-01-27  46000.0  ...        5.0       5.0        1.0     1.0   \n",
       "215350       2018-01-27  46000.0  ...        5.0       5.0        1.0     1.0   \n",
       "249912       2018-01-27  46000.0  ...        3.0       7.0        1.0     1.0   \n",
       "258459       2018-01-27  46000.0  ...        3.0       7.0        1.0     1.0   \n",
       "\n",
       "        is_social  is_email  bogo  discount  informational  cum_amount  \n",
       "89            1.0       1.0   0.0       0.0            1.0        0.00  \n",
       "17664         0.0       0.0   0.0       0.0            0.0        3.75  \n",
       "21835         1.0       1.0   0.0       0.0            1.0        3.75  \n",
       "23730         0.0       0.0   0.0       0.0            0.0        5.08  \n",
       "45937         0.0       0.0   0.0       0.0            0.0       12.04  \n",
       "51564         0.0       0.0   0.0       0.0            0.0       13.40  \n",
       "109489        1.0       1.0   1.0       0.0            0.0       13.40  \n",
       "137605        1.0       1.0   1.0       0.0            0.0       13.40  \n",
       "148647        1.0       1.0   1.0       0.0            0.0       13.40  \n",
       "165231        1.0       1.0   1.0       0.0            0.0       13.40  \n",
       "165232        0.0       0.0   0.0       0.0            0.0       16.77  \n",
       "206982        1.0       1.0   1.0       0.0            0.0       16.77  \n",
       "215350        1.0       1.0   1.0       0.0            0.0       16.77  \n",
       "249912        1.0       1.0   0.0       1.0            0.0       16.77  \n",
       "258459        1.0       1.0   0.0       1.0            0.0       16.77  \n",
       "\n",
       "[15 rows x 22 columns]"
      ]
     },
     "execution_count": 1507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "person_data(final_df, 89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1514,
   "metadata": {},
   "outputs": [],
   "source": [
    "p11 = None\n",
    "p11 = figure(title=\"Cumulative spend over time (in days)\", \n",
    "            x_axis_label='timestamp in days', \n",
    "            y_axis_label = 'Cumulative spend in $',\n",
    "            plot_width=950, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "yticks = np.array([0, 25, 50, 75, 100, 200, 300])\n",
    "p11.yaxis.ticker = yticks    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1515,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_customer_journey(df, person, canvas, tnx_color='black', line_color='pink', is_show = True):\n",
    "    '''\n",
    "    This function\n",
    "     - plots a customer interaction with offers via chart showing transacation value against time stamp\n",
    "     \n",
    "     Input:\n",
    "      - person : person id as string or index of person in df as integer\n",
    "      - canvas : bokeh canvas object to draw journey\n",
    "      - tnx_color : color of transaction events\n",
    "      - line_color : color of step lines\n",
    "      - is_show : Boolean flag indicating if the plot/journey should be shown or canvas should be returned\n",
    "      \n",
    "      Return:\n",
    "      - canvas object containing customer jounery. This canvas can be used to combing another customer \n",
    "        journey by calling this function on that customer with same canvas.\n",
    "    '''\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "    lt =['transaction', 'offer received', 'offer viewed','offer completed']\n",
    "    markers = ['circle', 'inverted_triangle', 'triangle', 'x']\n",
    "    colors = [tnx_color, 'chocolate', 'darkcyan', 'salmon']\n",
    "    \n",
    "    p10 = canvas\n",
    "    p10.legend.location = 'bottom_right'\n",
    "    \n",
    "    if type(person) == str:\n",
    "        person_label = person[0:3]\n",
    "    else:\n",
    "        person_label = str(person)\n",
    "    \n",
    "    for i, event in enumerate(lt):\n",
    "        \n",
    "        if event == 'transaction':\n",
    "            x.append(person_data(df, person).time/24)\n",
    "            y.append(person_data(df,person).cum_amount)\n",
    "            p10.step(x[i], y[i],line_width =0.8 , color= line_color , legend_label = 'spend amount: ' + person_label )\n",
    "            p10.scatter(x[i], y[i], marker= markers[i], color = colors[i], legend_label = event +': ' + person_label , size=5)\n",
    "            \n",
    "        else:\n",
    "            try:\n",
    "                x.append(person_data(df, person)[person_data(df, person).event == event].time/24)\n",
    "                y.append(person_data(df, person)[person_data(df, person).event == event].cum_amount)\n",
    "                p10.scatter(x[i], y[i], marker= markers[i], color = colors[i], legend_label = event, size=10)\n",
    "            except:\n",
    "                pass\n",
    "            \n",
    "         \n",
    "        if event == 'offer received':\n",
    "                      \n",
    "            received = person_data(df, person)[person_data(df, person).event=='offer received']\\\n",
    "                                          [['time', 'difficulty', 'cum_amount', 'duration']]\\\n",
    "                                          .reset_index()\n",
    "            \n",
    "            for i in received.index:\n",
    "                x_diff = [received.iloc[i].time/24, \n",
    "                          received.iloc[i].time/24 + received.iloc[i].duration]\n",
    "                y_diff = [received.iloc[i].cum_amount, \n",
    "                          received.iloc[i].cum_amount + received.iloc[i].difficulty]\n",
    "                p10.line(x_diff, y_diff, color='navy',line_width =0.5, legend_label = 'offer duration')\n",
    "    if is_show == False:\n",
    "        return p10\n",
    "    else:\n",
    "        show(p10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1518,
   "metadata": {},
   "outputs": [],
   "source": [
    "p11 = plot_customer_journey(final_df, 57, p11, 'olivedrab', 'pink', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1519,
   "metadata": {},
   "outputs": [
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       "  var render_items = [{\"docid\":\"a004172c-481a-4be6-a835-7f773ac49e23\",\"root_ids\":[\"1181467\"],\"roots\":{\"1181467\":\"232df3ab-a378-4af4-a429-d9d7b7ac29a9\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1181467"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_file(\"dual_customers_journey.html\")\n",
    "plot_customer_journey(final_df, 52, p11, 'darkkhaki', 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "- Here in the above chart, we have compared the journey of two random customers. There is a significant difference in the spending behavior of these two customers. \n",
    "- For customer 57, he/she views each offers almost immediately and has made some high values transactions. It naturally leads to offer completion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_of_offers_completed(person_data_df):\n",
    "    '''\n",
    "    This function\n",
    "     - calculate the percentage of offers completed by a particular customer \n",
    "     \n",
    "     Input:\n",
    "      - person_data_df is dataframe containing offer and transaction information for a single person\n",
    "     Output:\n",
    "       - percentage count value as floting point number\n",
    "    '''\n",
    "    \n",
    "    percent_count = 0\n",
    "    \n",
    "    sent_offer_count = 0\n",
    "    \n",
    "    completed_offer_count = 0\n",
    "    \n",
    "    for x in person_data_df.event:\n",
    "        \n",
    "        if x == 'offer received':\n",
    "            sent_offer_count += 1\n",
    "        \n",
    "        if x == 'offer completed':\n",
    "            completed_offer_count += 1\n",
    "            \n",
    "    if sent_offer_count == 0:\n",
    "        percent_count = 0\n",
    "    else:\n",
    "        percent_count = (completed_offer_count/sent_offer_count) * 100\n",
    "    \n",
    "    return percent_count\n",
    "\n",
    "\n",
    "def absolute_offers_completed(person_data_df):\n",
    "    '''\n",
    "    This function\n",
    "     - calculates the absolute number of offers completed by a particular customer without considering if the\n",
    "        offer is viewed before completion or not\n",
    "     \n",
    "     Input:\n",
    "      - person_data_df is dataframe containing offer and transaction information for a single person\n",
    "     Output:\n",
    "       - offer completion count value as integer\n",
    "    '''\n",
    "     \n",
    "    completed_offer_count = 0\n",
    "    \n",
    "    for x in person_data_df.event:\n",
    "    \n",
    "        if x == 'offer completed':\n",
    "            completed_offer_count += 1\n",
    "            \n",
    "    return completed_offer_count\n",
    "\n",
    "\n",
    "\n",
    "def percent_of_offers_viewed(person_data_df):\n",
    "    '''\n",
    "    This function\n",
    "     - calculate the percentage of offers viewed by a particular customer \n",
    "     \n",
    "     Input:\n",
    "      - person_data_df is dataframe containing offer and transaction information for a single person\n",
    "     Output:\n",
    "       - percentage count value as floting point number\n",
    "    '''\n",
    "    \n",
    "    \n",
    "    percent_count = 0\n",
    "    \n",
    "    sent_offer_count = 0\n",
    "    \n",
    "    viewed_offer_count = 0\n",
    "    \n",
    "    for x in person_data_df.event:\n",
    "        \n",
    "        if x == 'offer received':\n",
    "            sent_offer_count += 1\n",
    "        \n",
    "        if x == 'offer viewed':\n",
    "            viewed_offer_count += 1\n",
    "            \n",
    "    if sent_offer_count == 0:\n",
    "        percent_count = 0\n",
    "    else:\n",
    "        percent_count = (viewed_offer_count/sent_offer_count) * 100\n",
    "    \n",
    "    return percent_count\n",
    "    \n",
    "    \n",
    "\n",
    "def absolute_offers_viewed(person_data_df):\n",
    "     '''\n",
    "    This function\n",
    "     - calculate the number of offers viewed by a particular customer \n",
    "     \n",
    "     Input:\n",
    "      - person_data_df is dataframe containing offer and transaction information for a single person\n",
    "     Output:\n",
    "       - offer viewed count value as integer\n",
    "    '''\n",
    "    \n",
    "    viewed_offer_count = 0\n",
    "    \n",
    "    for x in person_data_df.event:\n",
    "    \n",
    "        if x == 'offer viewed':\n",
    "            viewed_offer_count += 1\n",
    "            \n",
    "    return viewed_offer_count\n",
    "\n",
    "\n",
    "#https://stackoverflow.com/questions/72899/how-do-i-sort-a-list-of-dictionaries-by-a-value-of-the-dictionary\n",
    "def sort_key_func(item):\n",
    "    \"\"\" helper function used to sort list of dicts\n",
    "\n",
    "    :param item: dict\n",
    "    :return: sorted list of tuples (k, v)\n",
    "    \"\"\"\n",
    "    pairs = []\n",
    "    for k, v in item.items():\n",
    "        pairs.append(v)\n",
    "    return sorted(pairs)\n",
    "\n",
    "\n",
    "def find_most_responsive_customer(df, limit):\n",
    "    '''\n",
    "    This function should provide a list of dictionaries specifying customer id \n",
    "    and number of completed offers (weighted by number of offers sent) for that customer\n",
    "    in a given dataframe sorted by values of completed offer\n",
    "    '''\n",
    "    person_list = df.person.unique().tolist()\n",
    "    \n",
    "    if limit != None:\n",
    "        if type(limit) == int:\n",
    "            person_list = person_list[:limit]\n",
    "       \n",
    "    percent_offers_completed = []\n",
    "    abs_offers_completed = []\n",
    "    percent_offers_viewed = [] \n",
    "    abs_offers_viewed = []\n",
    "    \n",
    "    for person in person_list:\n",
    "        \n",
    "        element_p_o_c = {}\n",
    "        element_a_o_c = {}\n",
    "        element_p_o_v = {}\n",
    "        element_a_o_v = {}\n",
    "        \n",
    "        person_data_df = person_data(df, person)\n",
    "        \n",
    "        value_p_o_c = percent_of_offers_completed(person_data_df)\n",
    "        \n",
    "        value_a_o_c = absolute_offers_completed(person_data_df)\n",
    "        \n",
    "        value_p_o_v = percent_of_offers_viewed(person_data_df)\n",
    "        \n",
    "        value_a_o_v = absolute_offers_viewed(person_data_df)\n",
    "          \n",
    "        if type(person) == str:\n",
    "            \n",
    "            element_p_o_c[person] = value_p_o_c\n",
    "            percent_offers_completed.append(element_p_o_c)\n",
    "            \n",
    "            element_a_o_c[person] = value_a_o_c\n",
    "            abs_offers_completed.append(element_a_o_c)\n",
    "            \n",
    "            element_p_o_v[person] = value_p_o_v\n",
    "            percent_offers_viewed.append(element_p_o_v)\n",
    "            \n",
    "            element_a_o_v[person] = value_a_o_v\n",
    "            abs_offers_viewed.append(element_a_o_v)\n",
    "            \n",
    "        else:\n",
    "            continue\n",
    "            \n",
    "    percent_offers_completed =sorted(percent_offers_completed, key=sort_key_func, reverse=True)\n",
    "    abs_offers_completed = sorted(abs_offers_completed, key=sort_key_func, reverse=True)\n",
    "    percent_offers_viewed = sorted(percent_offers_viewed, key=sort_key_func, reverse=True)\n",
    "    abs_offers_viewed = sorted(abs_offers_viewed, key=sort_key_func, reverse=True)\n",
    "    \n",
    "    return percent_offers_completed, abs_offers_completed, percent_offers_viewed, abs_offers_viewed\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_offers_completed, abs_offers_completed, percent_offers_viewed, abs_offers_viewed = find_most_responsive_customer(final_df, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'6e014185620b49bd98749f728747572f': 100.0},\n",
       " {'9fa9ae8f57894cc9a3b8a9bbe0fc1b2f': 100.0},\n",
       " {'fe8264108d5b4f198453bbb1fa7ca6c9': 100.0},\n",
       " {'b7a66e629b134079a9bc7120c7b9947b': 100.0},\n",
       " {'ca82e1ebc759402c8ab95c341755bdf1': 100.0}]"
      ]
     },
     "execution_count": 1014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_offers_completed[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'f8aedd0cbea0419c806842b4265b82e5': 6},\n",
       " {'8c410d84af08408fb41f953c93ffac27': 6},\n",
       " {'b3ad8755d0ac47faa6e9c10954fec6a4': 6},\n",
       " {'c93f9619abd642a684bd79953cef992c': 6},\n",
       " {'ca369d4c5c0c4bb996bf78ec0b65775b': 6}]"
      ]
     },
     "execution_count": 1017,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_offers_completed[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'78afa995795e4d85b5d9ceeca43f5fef': 100.0},\n",
       " {'389bc3fa690240e798340f5a15918d5c': 100.0},\n",
       " {'c4863c7985cf408faee930f111475da3': 100.0},\n",
       " {'aa4862eba776480b8bb9c68455b8c2e1': 100.0},\n",
       " {'744d603ef08c4f33af5a61c8c7628d1c': 100.0}]"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_offers_viewed[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'389bc3fa690240e798340f5a15918d5c': 6},\n",
       " {'c27e0d6ab72c455a8bb66d980963de60': 6},\n",
       " {'d058f73bf8674a26a95227db098147b1': 6},\n",
       " {'1e9420836d554513ab90eba98552d0a9': 6},\n",
       " {'102e9454054946fda62242d2e176fdce': 6}]"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_offers_viewed[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "\n",
    "The above analysis suggests that maximum 6 offers were sent out to at least one customer in 30 days period.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1520,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_persons = final_df.person.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16578"
      ]
     },
     "execution_count": 1521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there exist a 16578 unique customers in the dataset\n",
    "len(unique_persons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1522,
   "metadata": {},
   "outputs": [
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       "  var render_items = [{\"docid\":\"8fbf77fa-5d4d-4825-bff8-2672eb0daaeb\",\"root_ids\":[\"1296596\"],\"roots\":{\"1296596\":\"297bb516-47e1-43dc-8090-539d58d0f269\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "    }, 10, root)\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1296596"
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     "output_type": "display_data"
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   ],
   "source": [
    "p7 = None\n",
    "output_file('single_customer_journey.html')\n",
    "p7 = figure(title=\"Cumulative spend over time (in days)\", \n",
    "            x_axis_label='timestamp in days', \n",
    "            y_axis_label = 'Cumulative spend in $',\n",
    "            plot_width=950, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "\n",
    "plot_customer_journey(final_df, '6e014185620b49bd98749f728747572f', p7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "- Here in the above chart, we have a customer who has completed all the offers sent out to him/her.  It usually takes more than one transaction to complete an offer if those transactions are low value.\n",
    "\n",
    "- Sometimes, the customer views the offer instantly while sometimes, he/she views it at a later time.\n",
    "\n",
    "- Out of 4 offers completed, only 1 (last completed) is not viewed by the customer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1523,
   "metadata": {},
   "outputs": [],
   "source": [
    "p12 = None\n",
    "output_file(\"dual_high_value_customers.html\")\n",
    "p12 = figure(title= \"                   Cumulative spend over time (in days) \", \n",
    "            x_axis_label='timestamp in days', \n",
    "            y_axis_label = 'Cumulative spend in $',\n",
    "            plot_width=950, tools= \"pan,wheel_zoom,box_zoom,reset,hover\"\n",
    "            )\n",
    "#yticks = np.array([0, 25, 50, 75, 100, 200, 300])\n",
    "#p12.yaxis.ticker = yticks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1524,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       "\n",
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      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1299873"
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     },
     "output_type": "display_data"
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   ],
   "source": [
    "p12 = plot_customer_journey(feat_df_2, '9fa9ae8f57894cc9a3b8a9bbe0fc1b2f', p12, 'olivedrab', 'pink', False)\n",
    "plot_customer_journey(feat_df_2, 'fe8264108d5b4f198453bbb1fa7ca6c9', p12, 'indigo', 'skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Observation*\n",
    "- Here we have two high paying customers side by side who has completed all the received offers.\n",
    "\n",
    "- Interestingly, for customer '9fa' after completing the first offer, he/she has made 5 transactions amounting in total around 75 USD without any pending offer to complete. It demonstrates that those transactions were not motivated to complete the offer and show customers spending tendencies in the absence of any offers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_total_spend_amount(df, limit = None):\n",
    "    '''\n",
    "    This function\n",
    "     - calculates a total amount spent by each customer during the time frame of 30 days\n",
    "     \n",
    "    Input:\n",
    "      df: input dataframe containing all the customers and correponding offer and transcation information\n",
    "      limit: integer value to limit the operation for only limited number of customer\n",
    "             It will slice the dataframe first (selecting limited rows) and then performs calculation\n",
    "    Output:\n",
    "      Sorted array based on amount value where each element is a key-value pair i.e.{person_id, spend_amount}\n",
    "    '''\n",
    "    \n",
    "    person_list = df.person.unique().tolist()\n",
    "    \n",
    "    if limit != None:\n",
    "        if type(limit) == int:\n",
    "            person_list = person_list[:limit]\n",
    "    \n",
    "    amounts = []\n",
    "    \n",
    "    for person in person_list:\n",
    "        \n",
    "        element = {}\n",
    "        \n",
    "        person_data_df = person_data(df, person)\n",
    "        \n",
    "        max_amount = person_data_df.cum_amount.max()\n",
    "          \n",
    "        if type(person) == str:\n",
    "            \n",
    "            element[person] = max_amount\n",
    "            amounts.append(element)\n",
    "    amounts = sorted(amounts, key=sort_key_func, reverse=True)\n",
    "    \n",
    "    return amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [],
   "source": [
    "amounts = find_total_spend_amount(final_df, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16578"
      ]
     },
     "execution_count": 1044,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(amounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd1 = pd.DataFrame.from_dict(amounts[:5], orient='columns', dtype=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [
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       "                                       0       1       2       3       4\n",
       "7eef6a95040a49e2aa8863beb943d381  497.98     NaN     NaN     NaN     NaN\n",
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     "metadata": {},
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   ]
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  {
   "cell_type": "code",
   "execution_count": 1107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def create_dataframe_from_list(input_list, name):\n",
    "    '''\n",
    "    This function\n",
    "     - creates a dataframe out of a list having dictionary elements. Key of each element is person_id and \n",
    "     value is corresponding target value (i.e. total spend amount, percent of offers completed)\n",
    "     \n",
    "    '''\n",
    "    df1 = pd.DataFrame.from_dict(input_list, orient='columns').astype(float).sort_index() \n",
    "    df1 = df1.T\n",
    "    df1[name] = df1.sum(axis=1)\n",
    "    df1 = df1[name]\n",
    "    df2 = pd.DataFrame([df1])\n",
    "    df = df2.T\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd3 = pd2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = create_dataframe_from_list(amounts, 'spend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "metadata": {},
   "outputs": [
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       "                                   spend\n",
       "7eef6a95040a49e2aa8863beb943d381  497.98\n",
       "8964bdf23d544301846b95a02d27e45c  492.34\n",
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       "...                                  ...\n",
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       "d75d726171364516998e49813c2b4671    0.00\n",
       "\n",
       "[16578 rows x 1 columns]"
      ]
     },
     "execution_count": 1110,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "amounts_df = df\n",
    "amounts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [
    {
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       "        spend                            person\n",
       "0      497.98  7eef6a95040a49e2aa8863beb943d381\n",
       "1      492.34  8964bdf23d544301846b95a02d27e45c\n",
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       "...       ...                               ...\n",
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       "16577    0.00  d75d726171364516998e49813c2b4671\n",
       "\n",
       "[16578 rows x 2 columns]"
      ]
     },
     "execution_count": 1133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amounts_df['person'] = amounts_df.index\n",
    "amounts_df = amounts_df.reset_index(drop=True) \n",
    "amounts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_offers_viewed_df  = create_dataframe_from_list(percent_offers_viewed, 'percent_offers_viewed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [
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       "                                  percent_offers_viewed\n",
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     "metadata": {},
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   "source": [
    "percent_offers_viewed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {},
   "outputs": [
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       "       percent_offers_viewed                            person\n",
       "0                      100.0  78afa995795e4d85b5d9ceeca43f5fef\n",
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       "16577                    0.0  da7a7c0dcfcb41a8acc7864a53cf60fb\n",
       "\n",
       "[16578 rows x 2 columns]"
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     },
     "execution_count": 1132,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "percent_offers_viewed_df['person'] = percent_offers_viewed_df.index\n",
    "percent_offers_viewed_df = percent_offers_viewed_df.reset_index(drop=True) \n",
    "percent_offers_viewed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [],
   "source": [
    "abs_offers_viewed_df  = create_dataframe_from_list(abs_offers_viewed, 'abs_offers_viewed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
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       "                                  abs_offers_viewed\n",
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       "\n",
       "[16578 rows x 1 columns]"
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     "execution_count": 1114,
     "metadata": {},
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  {
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   "execution_count": 1131,
   "metadata": {},
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       "       abs_offers_viewed                            person\n",
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     "execution_count": 1131,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "abs_offers_viewed_df['person'] = abs_offers_viewed_df.index\n",
    "abs_offers_viewed_df = abs_offers_viewed_df.reset_index(drop=True) \n",
    "abs_offers_viewed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_offers_completed_df  = create_dataframe_from_list(percent_offers_completed, 'percent_offers_completed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "metadata": {},
   "outputs": [
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    "percent_offers_completed_df  "
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  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [
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       "       percent_offers_completed                            person\n",
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       "\n",
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     "execution_count": 1130,
     "metadata": {},
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   "source": [
    "percent_offers_completed_df['person'] = percent_offers_completed_df.index\n",
    "percent_offers_completed_df = percent_offers_completed_df.reset_index(drop=True) \n",
    "percent_offers_completed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {},
   "outputs": [],
   "source": [
    "abs_offers_completed_df  = create_dataframe_from_list(abs_offers_completed, 'abs_offers_completed')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {},
   "outputs": [
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       "       abs_offers_completed                            person\n",
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       "\n",
       "[16578 rows x 2 columns]"
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     },
     "execution_count": 1124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_offers_completed_df['person'] = abs_offers_completed_df.index\n",
    "abs_offers_completed_df = abs_offers_completed_df.reset_index(drop=True) \n",
    "abs_offers_completed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {},
   "outputs": [
    {
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       "                                  person           event  time  amount  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef  offer received     0    0.00   \n",
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       "...                                  ...             ...   ...     ...   \n",
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       "\n",
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       "\n",
       "        became_member_on    income  ...  rewards_p  duration  is_mobile  \\\n",
       "0               20170509  100000.0  ...        5.0       7.0        1.0   \n",
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       "3               20171002       0.0  ...       10.0       5.0        1.0   \n",
       "4               20180209   53000.0  ...        5.0       5.0        1.0   \n",
       "...                  ...       ...  ...        ...       ...        ...   \n",
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       "302454          20151211       0.0  ...        0.0       0.0        0.0   \n",
       "\n",
       "        is_web  is_social  is_email  bogo  discount  informational  cum_amount  \n",
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       "4          1.0        1.0       1.0   1.0       0.0            0.0        0.00  \n",
       "...        ...        ...       ...   ...       ...            ...         ...  \n",
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       "302451     0.0        0.0       0.0   0.0       0.0            0.0       10.01  \n",
       "302452     0.0        0.0       0.0   0.0       0.0            0.0       63.05  \n",
       "302453     0.0        0.0       0.0   0.0       0.0            0.0       23.02  \n",
       "302454     0.0        0.0       0.0   0.0       0.0            0.0       12.78  \n",
       "\n",
       "[302455 rows x 21 columns]"
      ]
     },
     "execution_count": 1125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1312,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>68</td>\n",
       "      <td>20180426</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>20171002</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>offer received</td>\n",
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       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
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       "      <td>20180209</td>\n",
       "      <td>53000.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254243</th>\n",
       "      <td>d087c473b4d247ccb0abfef59ba12b0e</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>20161023</td>\n",
       "      <td>51000.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.01</td>\n",
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       "    <tr>\n",
       "      <th>254244</th>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>60</td>\n",
       "      <td>20180505</td>\n",
       "      <td>113000.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.59</td>\n",
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       "    <tr>\n",
       "      <th>254245</th>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
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       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>20180604</td>\n",
       "      <td>54000.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>13.27</td>\n",
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       "    <tr>\n",
       "      <th>254246</th>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>20160307</td>\n",
       "      <td>50000.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>131.17</td>\n",
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       "    <tr>\n",
       "      <th>254247</th>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>20170722</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person           event  time  amount  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef  offer received     0     0.0   \n",
       "1       a03223e636434f42ac4c3df47e8bac43  offer received     0     0.0   \n",
       "2       e2127556f4f64592b11af22de27a7932  offer received     0     0.0   \n",
       "3       68617ca6246f4fbc85e91a2a49552598  offer received     0     0.0   \n",
       "4       389bc3fa690240e798340f5a15918d5c  offer received     0     0.0   \n",
       "...                                  ...             ...   ...     ...   \n",
       "254243  d087c473b4d247ccb0abfef59ba12b0e  offer received   576     0.0   \n",
       "254244  cb23b66c56f64b109d673d5e56574529  offer received   576     0.0   \n",
       "254245  6d5f3a774f3d4714ab0c092238f3a1d7  offer received   576     0.0   \n",
       "254246  9dc1421481194dcd9400aec7c9ae6366  offer received   576     0.0   \n",
       "254247  e4052622e5ba45a8b96b59aba68cf068  offer received   576     0.0   \n",
       "\n",
       "                                offer_id  rewards_t gender  age  \\\n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9        0.0      F   75   \n",
       "1       0b1e1539f2cc45b7b9fa7c272da2e1d7        0.0      0  118   \n",
       "2       2906b810c7d4411798c6938adc9daaa5        0.0      M   68   \n",
       "3       4d5c57ea9a6940dd891ad53e9dbe8da0        0.0      0  118   \n",
       "4       f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   65   \n",
       "...                                  ...        ...    ...  ...   \n",
       "254243  ae264e3637204a6fb9bb56bc8210ddfd        0.0      M   56   \n",
       "254244  2906b810c7d4411798c6938adc9daaa5        0.0      M   60   \n",
       "254245  2298d6c36e964ae4a3e7e9706d1fb8c2        0.0      F   45   \n",
       "254246  ae264e3637204a6fb9bb56bc8210ddfd        0.0      F   83   \n",
       "254247  3f207df678b143eea3cee63160fa8bed        0.0      F   62   \n",
       "\n",
       "        became_member_on    income  ...  rewards_p  duration  is_mobile  \\\n",
       "0               20170509  100000.0  ...        5.0       7.0        1.0   \n",
       "1               20170804       0.0  ...        5.0      10.0        0.0   \n",
       "2               20180426   70000.0  ...        2.0       7.0        1.0   \n",
       "3               20171002       0.0  ...       10.0       5.0        1.0   \n",
       "4               20180209   53000.0  ...        5.0       5.0        1.0   \n",
       "...                  ...       ...  ...        ...       ...        ...   \n",
       "254243          20161023   51000.0  ...       10.0       7.0        1.0   \n",
       "254244          20180505  113000.0  ...        2.0       7.0        1.0   \n",
       "254245          20180604   54000.0  ...        3.0       7.0        1.0   \n",
       "254246          20160307   50000.0  ...       10.0       7.0        1.0   \n",
       "254247          20170722   82000.0  ...        0.0       4.0        1.0   \n",
       "\n",
       "        is_web  is_social  is_email  bogo  discount  informational  cum_amount  \n",
       "0          1.0        0.0       1.0   1.0       0.0            0.0        0.00  \n",
       "1          1.0        0.0       1.0   0.0       1.0            0.0        0.00  \n",
       "2          1.0        0.0       1.0   0.0       1.0            0.0        0.00  \n",
       "3          1.0        1.0       1.0   1.0       0.0            0.0        0.00  \n",
       "4          1.0        1.0       1.0   1.0       0.0            0.0        0.00  \n",
       "...        ...        ...       ...   ...       ...            ...         ...  \n",
       "254243     0.0        1.0       1.0   1.0       0.0            0.0       72.01  \n",
       "254244     1.0        0.0       1.0   0.0       1.0            0.0      115.59  \n",
       "254245     1.0        1.0       1.0   0.0       1.0            0.0       13.27  \n",
       "254246     0.0        1.0       1.0   1.0       0.0            0.0      131.17  \n",
       "254247     1.0        0.0       1.0   0.0       0.0            1.0      118.31  \n",
       "\n",
       "[74396 rows x 21 columns]"
      ]
     },
     "execution_count": 1312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df = feat_df_2.loc[feat_df_2['event'].isin(['offer received'])]\n",
    "target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1313,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.merge(abs_offers_completed_df, left_on='person', right_on='person',\n",
    "                                 how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1314,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74391</th>\n",
       "      <td>d087c473b4d247ccb0abfef59ba12b0e</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>20161023</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.01</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74392</th>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>60</td>\n",
       "      <td>20180505</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.59</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74393</th>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>20180604</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74394</th>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>20160307</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74395</th>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>20170722</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.31</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person           event  time  amount  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef  offer received     0     0.0   \n",
       "1      a03223e636434f42ac4c3df47e8bac43  offer received     0     0.0   \n",
       "2      e2127556f4f64592b11af22de27a7932  offer received     0     0.0   \n",
       "3      68617ca6246f4fbc85e91a2a49552598  offer received     0     0.0   \n",
       "4      389bc3fa690240e798340f5a15918d5c  offer received     0     0.0   \n",
       "...                                 ...             ...   ...     ...   \n",
       "74391  d087c473b4d247ccb0abfef59ba12b0e  offer received   576     0.0   \n",
       "74392  cb23b66c56f64b109d673d5e56574529  offer received   576     0.0   \n",
       "74393  6d5f3a774f3d4714ab0c092238f3a1d7  offer received   576     0.0   \n",
       "74394  9dc1421481194dcd9400aec7c9ae6366  offer received   576     0.0   \n",
       "74395  e4052622e5ba45a8b96b59aba68cf068  offer received   576     0.0   \n",
       "\n",
       "                               offer_id  rewards_t gender  age  \\\n",
       "0      9b98b8c7a33c4b65b9aebfe6a799e6d9        0.0      F   75   \n",
       "1      0b1e1539f2cc45b7b9fa7c272da2e1d7        0.0      0  118   \n",
       "2      2906b810c7d4411798c6938adc9daaa5        0.0      M   68   \n",
       "3      4d5c57ea9a6940dd891ad53e9dbe8da0        0.0      0  118   \n",
       "4      f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   65   \n",
       "...                                 ...        ...    ...  ...   \n",
       "74391  ae264e3637204a6fb9bb56bc8210ddfd        0.0      M   56   \n",
       "74392  2906b810c7d4411798c6938adc9daaa5        0.0      M   60   \n",
       "74393  2298d6c36e964ae4a3e7e9706d1fb8c2        0.0      F   45   \n",
       "74394  ae264e3637204a6fb9bb56bc8210ddfd        0.0      F   83   \n",
       "74395  3f207df678b143eea3cee63160fa8bed        0.0      F   62   \n",
       "\n",
       "       became_member_on    income  ...  duration  is_mobile  is_web  \\\n",
       "0              20170509  100000.0  ...       7.0        1.0     1.0   \n",
       "1              20170804       0.0  ...      10.0        0.0     1.0   \n",
       "2              20180426   70000.0  ...       7.0        1.0     1.0   \n",
       "3              20171002       0.0  ...       5.0        1.0     1.0   \n",
       "4              20180209   53000.0  ...       5.0        1.0     1.0   \n",
       "...                 ...       ...  ...       ...        ...     ...   \n",
       "74391          20161023   51000.0  ...       7.0        1.0     0.0   \n",
       "74392          20180505  113000.0  ...       7.0        1.0     1.0   \n",
       "74393          20180604   54000.0  ...       7.0        1.0     1.0   \n",
       "74394          20160307   50000.0  ...       7.0        1.0     0.0   \n",
       "74395          20170722   82000.0  ...       4.0        1.0     1.0   \n",
       "\n",
       "       is_social  is_email  bogo  discount  informational  cum_amount  \\\n",
       "0            0.0       1.0   1.0       0.0            0.0        0.00   \n",
       "1            0.0       1.0   0.0       1.0            0.0        0.00   \n",
       "2            0.0       1.0   0.0       1.0            0.0        0.00   \n",
       "3            1.0       1.0   1.0       0.0            0.0        0.00   \n",
       "4            1.0       1.0   1.0       0.0            0.0        0.00   \n",
       "...          ...       ...   ...       ...            ...         ...   \n",
       "74391        1.0       1.0   1.0       0.0            0.0       72.01   \n",
       "74392        0.0       1.0   0.0       1.0            0.0      115.59   \n",
       "74393        1.0       1.0   0.0       1.0            0.0       13.27   \n",
       "74394        1.0       1.0   1.0       0.0            0.0      131.17   \n",
       "74395        0.0       1.0   0.0       0.0            1.0      118.31   \n",
       "\n",
       "       abs_offers_completed  \n",
       "0                       3.0  \n",
       "1                       0.0  \n",
       "2                       2.0  \n",
       "3                       0.0  \n",
       "4                       5.0  \n",
       "...                     ...  \n",
       "74391                   4.0  \n",
       "74392                   1.0  \n",
       "74393                   0.0  \n",
       "74394                   3.0  \n",
       "74395                   2.0  \n",
       "\n",
       "[74396 rows x 22 columns]"
      ]
     },
     "execution_count": 1314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_offers_completed_df percent_offers_completed_df percent_offers_viewed_df amounts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1315,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.merge(amounts_df, left_on='person', right_on='person',\n",
    "                                 how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1316,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.merge(percent_offers_viewed_df, left_on='person', right_on='person',\n",
    "                                 how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1317,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.merge(percent_offers_completed_df, left_on='person', right_on='person',\n",
    "                                 how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1318,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.merge(abs_offers_viewed_df, left_on='person', right_on='person',\n",
    "                                 how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1156,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df =target_df.drop(['percent_offers_completed_y'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1328,
   "metadata": {},
   "outputs": [],
   "source": [
    "def memger_column_cleanup(df, save=None):\n",
    "    \n",
    "    #df= df.drop(['amount','rewards_t','is_email'], axis=1)\n",
    "    df.became_member_on = pd.to_datetime(df.became_member_on, format='%Y%m%d', errors='coerce')\n",
    "    df['member_since_x_days'] = (\n",
    "    pd.to_datetime('today') - df.became_member_on)\n",
    "    df['member_since_x_days'] = df['member_since_x_days'].dt.days\n",
    "    df =df.drop(['became_member_on'], axis=1)\n",
    "    \n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1320,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = memger_column_cleanup(target_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['person', 'event', 'time', 'amount', 'offer_id', 'rewards_t', 'gender',\n",
       "       'age', 'income', 'difficulty', 'rewards_p', 'duration', 'is_mobile',\n",
       "       'is_web', 'is_social', 'is_email', 'bogo', 'discount', 'informational',\n",
       "       'cum_amount', 'abs_offers_completed', 'spend', 'percent_offers_viewed',\n",
       "       'percent_offers_completed', 'abs_offers_viewed', 'member_since_x_days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>rewards_t</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>...</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>cum_amount</th>\n",
       "      <th>abs_offers_completed</th>\n",
       "      <th>spend</th>\n",
       "      <th>percent_offers_viewed</th>\n",
       "      <th>percent_offers_completed</th>\n",
       "      <th>abs_offers_viewed</th>\n",
       "      <th>member_since_x_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>159.27</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>4.65</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>68</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.73</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.24</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36.43</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74391</th>\n",
       "      <td>d087c473b4d247ccb0abfef59ba12b0e</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>107.56</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74392</th>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M</td>\n",
       "      <td>60</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.59</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74393</th>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.03</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74394</th>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>189.67</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74395</th>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>offer received</td>\n",
       "      <td>576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>143.02</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person           event  time  amount  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef  offer received     0     0.0   \n",
       "1      a03223e636434f42ac4c3df47e8bac43  offer received     0     0.0   \n",
       "2      e2127556f4f64592b11af22de27a7932  offer received     0     0.0   \n",
       "3      68617ca6246f4fbc85e91a2a49552598  offer received     0     0.0   \n",
       "4      389bc3fa690240e798340f5a15918d5c  offer received     0     0.0   \n",
       "...                                 ...             ...   ...     ...   \n",
       "74391  d087c473b4d247ccb0abfef59ba12b0e  offer received   576     0.0   \n",
       "74392  cb23b66c56f64b109d673d5e56574529  offer received   576     0.0   \n",
       "74393  6d5f3a774f3d4714ab0c092238f3a1d7  offer received   576     0.0   \n",
       "74394  9dc1421481194dcd9400aec7c9ae6366  offer received   576     0.0   \n",
       "74395  e4052622e5ba45a8b96b59aba68cf068  offer received   576     0.0   \n",
       "\n",
       "                               offer_id  rewards_t gender  age    income  \\\n",
       "0      9b98b8c7a33c4b65b9aebfe6a799e6d9        0.0      F   75  100000.0   \n",
       "1      0b1e1539f2cc45b7b9fa7c272da2e1d7        0.0      0  118       0.0   \n",
       "2      2906b810c7d4411798c6938adc9daaa5        0.0      M   68   70000.0   \n",
       "3      4d5c57ea9a6940dd891ad53e9dbe8da0        0.0      0  118       0.0   \n",
       "4      f19421c1d4aa40978ebb69ca19b0e20d        0.0      M   65   53000.0   \n",
       "...                                 ...        ...    ...  ...       ...   \n",
       "74391  ae264e3637204a6fb9bb56bc8210ddfd        0.0      M   56   51000.0   \n",
       "74392  2906b810c7d4411798c6938adc9daaa5        0.0      M   60  113000.0   \n",
       "74393  2298d6c36e964ae4a3e7e9706d1fb8c2        0.0      F   45   54000.0   \n",
       "74394  ae264e3637204a6fb9bb56bc8210ddfd        0.0      F   83   50000.0   \n",
       "74395  3f207df678b143eea3cee63160fa8bed        0.0      F   62   82000.0   \n",
       "\n",
       "       difficulty  ...  bogo  discount  informational  cum_amount  \\\n",
       "0             5.0  ...   1.0       0.0            0.0        0.00   \n",
       "1            20.0  ...   0.0       1.0            0.0        0.00   \n",
       "2            10.0  ...   0.0       1.0            0.0        0.00   \n",
       "3            10.0  ...   1.0       0.0            0.0        0.00   \n",
       "4             5.0  ...   1.0       0.0            0.0        0.00   \n",
       "...           ...  ...   ...       ...            ...         ...   \n",
       "74391        10.0  ...   1.0       0.0            0.0       72.01   \n",
       "74392        10.0  ...   0.0       1.0            0.0      115.59   \n",
       "74393         7.0  ...   0.0       1.0            0.0       13.27   \n",
       "74394        10.0  ...   1.0       0.0            0.0      131.17   \n",
       "74395         0.0  ...   0.0       0.0            1.0      118.31   \n",
       "\n",
       "       abs_offers_completed   spend  percent_offers_viewed  \\\n",
       "0                       3.0  159.27             100.000000   \n",
       "1                       0.0    4.65              60.000000   \n",
       "2                       2.0   57.73              75.000000   \n",
       "3                       0.0    0.24              80.000000   \n",
       "4                       5.0   36.43             100.000000   \n",
       "...                     ...     ...                    ...   \n",
       "74391                   4.0  107.56              83.333333   \n",
       "74392                   1.0  115.59               0.000000   \n",
       "74393                   0.0   20.03             100.000000   \n",
       "74394                   3.0  189.67             100.000000   \n",
       "74395                   2.0  143.02              50.000000   \n",
       "\n",
       "       percent_offers_completed  abs_offers_viewed  member_since_x_days  \n",
       "0                     75.000000                4.0                 1079  \n",
       "1                      0.000000                3.0                  992  \n",
       "2                     50.000000                3.0                  727  \n",
       "3                      0.000000                4.0                  933  \n",
       "4                     83.333333                6.0                  803  \n",
       "...                         ...                ...                  ...  \n",
       "74391                 66.666667                5.0                 1277  \n",
       "74392                 33.333333                0.0                  718  \n",
       "74393                  0.000000                3.0                  688  \n",
       "74394                100.000000                3.0                 1507  \n",
       "74395                 50.000000                2.0                 1005  \n",
       "\n",
       "[74396 rows x 26 columns]"
      ]
     },
     "execution_count": 1322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1323,
   "metadata": {},
   "outputs": [],
   "source": [
    "interim_target_df = target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1324,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping original event column\n",
    "target_df.drop(['event'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1332,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "def save_file(df, name, path='../data/interim'):\n",
    "    '''\n",
    "    Helper function saves dataFrame to .joblib format\n",
    "    default path '../../data/interim'\n",
    "\n",
    "    Parameters\n",
    "    -----------\n",
    "    df: given dataFrame\n",
    "    name: filename as string value\n",
    "    '''\n",
    "\n",
    "    joblib.dump(df, path + '/' + name, compress=True)\n",
    "    joblib.dump(df, path + '/' + 'latest.joblib', compress=True)\n",
    "    print('saved as {}'.format(path + '/' + name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as ../data/interim/final_target_df_2304.joblib\n"
     ]
    }
   ],
   "source": [
    "save_file(target_df, name='final_target_df_2304.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1184,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import progressbar\n",
    "import catboost\n",
    "import joblib\n",
    "from catboost import CatBoostClassifier\n",
    "from catboost import Pool\n",
    "from catboost import MetricVisualizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import timeit\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, GroupKFold\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, \\\n",
    "recall_score, f1_score\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit, GroupShuffleSplit\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1254,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>rewards_p</th>\n",
       "      <th>duration</th>\n",
       "      <th>is_mobile</th>\n",
       "      <th>is_web</th>\n",
       "      <th>...</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>cum_amount</th>\n",
       "      <th>abs_offers_completed</th>\n",
       "      <th>spend</th>\n",
       "      <th>percent_offers_viewed</th>\n",
       "      <th>percent_offers_completed_x</th>\n",
       "      <th>abs_offers_viewed</th>\n",
       "      <th>member_since_x_days</th>\n",
       "      <th>responsive_customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>70000.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>57.73</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>727</td>\n",
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       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
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       "    <tr>\n",
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       "      <td>4.0</td>\n",
       "      <td>107.56</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74392</th>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>576</td>\n",
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       "      <td>60</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>74393</th>\n",
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       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>688</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74394</th>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>576</td>\n",
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       "      <td>10.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.17</td>\n",
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       "      <td>189.67</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1507</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>74395</th>\n",
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       "      <td>82000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>118.31</td>\n",
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       "      <td>143.02</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person  time gender  age    income  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef     0      F   75  100000.0   \n",
       "1      a03223e636434f42ac4c3df47e8bac43     0      0  118       0.0   \n",
       "2      e2127556f4f64592b11af22de27a7932     0      M   68   70000.0   \n",
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       "4      389bc3fa690240e798340f5a15918d5c     0      M   65   53000.0   \n",
       "...                                 ...   ...    ...  ...       ...   \n",
       "74391  d087c473b4d247ccb0abfef59ba12b0e   576      M   56   51000.0   \n",
       "74392  cb23b66c56f64b109d673d5e56574529   576      M   60  113000.0   \n",
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       "74395  e4052622e5ba45a8b96b59aba68cf068   576      F   62   82000.0   \n",
       "\n",
       "       difficulty  rewards_p  duration  is_mobile  is_web  ...  discount  \\\n",
       "0             5.0        5.0       7.0        1.0     1.0  ...       0.0   \n",
       "1            20.0        5.0      10.0        0.0     1.0  ...       1.0   \n",
       "2            10.0        2.0       7.0        1.0     1.0  ...       1.0   \n",
       "3            10.0       10.0       5.0        1.0     1.0  ...       0.0   \n",
       "4             5.0        5.0       5.0        1.0     1.0  ...       0.0   \n",
       "...           ...        ...       ...        ...     ...  ...       ...   \n",
       "74391        10.0       10.0       7.0        1.0     0.0  ...       0.0   \n",
       "74392        10.0        2.0       7.0        1.0     1.0  ...       1.0   \n",
       "74393         7.0        3.0       7.0        1.0     1.0  ...       1.0   \n",
       "74394        10.0       10.0       7.0        1.0     0.0  ...       0.0   \n",
       "74395         0.0        0.0       4.0        1.0     1.0  ...       0.0   \n",
       "\n",
       "       informational  cum_amount  abs_offers_completed   spend  \\\n",
       "0                0.0        0.00                   3.0  159.27   \n",
       "1                0.0        0.00                   0.0    4.65   \n",
       "2                0.0        0.00                   2.0   57.73   \n",
       "3                0.0        0.00                   0.0    0.24   \n",
       "4                0.0        0.00                   5.0   36.43   \n",
       "...              ...         ...                   ...     ...   \n",
       "74391            0.0       72.01                   4.0  107.56   \n",
       "74392            0.0      115.59                   1.0  115.59   \n",
       "74393            0.0       13.27                   0.0   20.03   \n",
       "74394            0.0      131.17                   3.0  189.67   \n",
       "74395            1.0      118.31                   2.0  143.02   \n",
       "\n",
       "       percent_offers_viewed  percent_offers_completed_x  abs_offers_viewed  \\\n",
       "0                 100.000000                   75.000000                4.0   \n",
       "1                  60.000000                    0.000000                3.0   \n",
       "2                  75.000000                   50.000000                3.0   \n",
       "3                  80.000000                    0.000000                4.0   \n",
       "4                 100.000000                   83.333333                6.0   \n",
       "...                      ...                         ...                ...   \n",
       "74391              83.333333                   66.666667                5.0   \n",
       "74392               0.000000                   33.333333                0.0   \n",
       "74393             100.000000                    0.000000                3.0   \n",
       "74394             100.000000                  100.000000                3.0   \n",
       "74395              50.000000                   50.000000                2.0   \n",
       "\n",
       "       member_since_x_days  responsive_customer  \n",
       "0                     1079                    1  \n",
       "1                      992                    0  \n",
       "2                      727                    0  \n",
       "3                      933                    0  \n",
       "4                      803                    1  \n",
       "...                    ...                  ...  \n",
       "74391                 1277                    0  \n",
       "74392                  718                    0  \n",
       "74393                  688                    0  \n",
       "74394                 1507                    1  \n",
       "74395                 1005                    0  \n",
       "\n",
       "[74396 rows x 22 columns]"
      ]
     },
     "execution_count": 1254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = joblib.load('../data/interim/final_target_df_2304.joblib')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person  time gender  age    income  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef     0      F   75  100000.0   \n",
       "1      a03223e636434f42ac4c3df47e8bac43     0      0  118       0.0   \n",
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       "4      389bc3fa690240e798340f5a15918d5c     0      M   65   53000.0   \n",
       "...                                 ...   ...    ...  ...       ...   \n",
       "74391  d087c473b4d247ccb0abfef59ba12b0e   576      M   56   51000.0   \n",
       "74392  cb23b66c56f64b109d673d5e56574529   576      M   60  113000.0   \n",
       "74393  6d5f3a774f3d4714ab0c092238f3a1d7   576      F   45   54000.0   \n",
       "74394  9dc1421481194dcd9400aec7c9ae6366   576      F   83   50000.0   \n",
       "74395  e4052622e5ba45a8b96b59aba68cf068   576      F   62   82000.0   \n",
       "\n",
       "       difficulty  rewards_p  duration  is_mobile  is_web  is_social  bogo  \\\n",
       "0             5.0        5.0       7.0        1.0     1.0        0.0   1.0   \n",
       "1            20.0        5.0      10.0        0.0     1.0        0.0   0.0   \n",
       "2            10.0        2.0       7.0        1.0     1.0        0.0   0.0   \n",
       "3            10.0       10.0       5.0        1.0     1.0        1.0   1.0   \n",
       "4             5.0        5.0       5.0        1.0     1.0        1.0   1.0   \n",
       "...           ...        ...       ...        ...     ...        ...   ...   \n",
       "74391        10.0       10.0       7.0        1.0     0.0        1.0   1.0   \n",
       "74392        10.0        2.0       7.0        1.0     1.0        0.0   0.0   \n",
       "74393         7.0        3.0       7.0        1.0     1.0        1.0   0.0   \n",
       "74394        10.0       10.0       7.0        1.0     0.0        1.0   1.0   \n",
       "74395         0.0        0.0       4.0        1.0     1.0        0.0   0.0   \n",
       "\n",
       "       discount  informational  cum_amount  abs_offers_completed   spend  \\\n",
       "0           0.0            0.0        0.00                   3.0  159.27   \n",
       "1           1.0            0.0        0.00                   0.0    4.65   \n",
       "2           1.0            0.0        0.00                   2.0   57.73   \n",
       "3           0.0            0.0        0.00                   0.0    0.24   \n",
       "4           0.0            0.0        0.00                   5.0   36.43   \n",
       "...         ...            ...         ...                   ...     ...   \n",
       "74391       0.0            0.0       72.01                   4.0  107.56   \n",
       "74392       1.0            0.0      115.59                   1.0  115.59   \n",
       "74393       1.0            0.0       13.27                   0.0   20.03   \n",
       "74394       0.0            0.0      131.17                   3.0  189.67   \n",
       "74395       0.0            1.0      118.31                   2.0  143.02   \n",
       "\n",
       "       percent_offers_viewed  abs_offers_viewed  member_since_x_days  \n",
       "0                 100.000000                4.0                 1079  \n",
       "1                  60.000000                3.0                  992  \n",
       "2                  75.000000                3.0                  727  \n",
       "3                  80.000000                4.0                  933  \n",
       "4                 100.000000                6.0                  803  \n",
       "...                      ...                ...                  ...  \n",
       "74391              83.333333                5.0                 1277  \n",
       "74392               0.000000                0.0                  718  \n",
       "74393             100.000000                3.0                  688  \n",
       "74394             100.000000                3.0                 1507  \n",
       "74395              50.000000                2.0                 1005  \n",
       "\n",
       "[74396 rows x 20 columns]"
      ]
     },
     "execution_count": 1255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.drop('percent_offers_completed_x', axis=1)\n",
    "X = new_df.drop('responsive_customer', axis=1)\n",
    "X = X.drop('abs_offers_completed', axis=1)\n",
    "y = new_df.responsive_customer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, 18])"
      ]
     },
     "execution_count": 1257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features_indices = np.where(X.dtypes != np.float)[0]\n",
    "categorical_features_indices "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1258,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False, \n",
    "                                                        random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1259,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pool = Pool(data=X_train, label=y_train, cat_features=categorical_features_indices)\n",
    "test_pool = Pool(data=X_test, label=y_test, cat_features=categorical_features_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1260,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = CatBoostClassifier(\n",
    "        iterations=1000,\n",
    "        loss_function='MultiClass',\n",
    "        custom_loss=['Accuracy'],\n",
    "        random_seed=42,\n",
    "        logging_level='Silent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1261,
   "metadata": {},
   "outputs": [
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac38194c83924f8e91d99bfd22f2d9fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1.fit(\n",
    "    train_pool,\n",
    "    eval_set= test_pool,\n",
    "#     logging_level='Verbose',  # you can uncomment this for text output\n",
    "    plot=True\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [0]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "preds_class = model1.predict(X_test)\n",
    "print(preds_class[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.18596634e-04 9.99881403e-01]\n",
      " [9.99583433e-01 4.16567494e-04]\n",
      " [9.99959435e-01 4.05654500e-05]\n",
      " [9.00493117e-05 9.99909951e-01]\n",
      " [9.99887579e-01 1.12420997e-04]\n",
      " [8.19116867e-01 1.80883133e-01]\n",
      " [9.99937516e-01 6.24841974e-05]\n",
      " [9.99978433e-01 2.15668207e-05]\n",
      " [9.99984070e-01 1.59303046e-05]\n",
      " [1.45666295e-04 9.99854334e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_probs = model1.predict_proba(X_test)\n",
    "print(predictions_probs[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.1171709969639778'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.9996639784946236'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10824     1]\n",
      " [    4  4051]]\n"
     ]
    }
   ],
   "source": [
    " display(F'Learning Rate set to: {model1.get_all_params()[\"learning_rate\"]}')\n",
    " display(F'Accuracy Score: {accuracy_score(y_test, preds_class)}')\n",
    " matrix = confusion_matrix(y_test, preds_class)\n",
    " print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = CatBoostClassifier(\n",
    "    custom_loss=['Accuracy'],\n",
    "    random_seed=42,\n",
    "    logging_level='Silent'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(\n",
    "    X_train, y_train,\n",
    "    cat_features=categorical_features_indices,\n",
    "    eval_set=(X_validation, y_validation),\n",
    "#     logging_level='Verbose',  # you can uncomment this for text output\n",
    "    plot=True\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1235,
   "metadata": {},
   "outputs": [
    {
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       "      <th>is_mobile</th>\n",
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       "      <th>...</th>\n",
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       "      <th>percent_offers_completed_x</th>\n",
       "      <th>abs_offers_viewed</th>\n",
       "      <th>member_since_x_days</th>\n",
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       "  </thead>\n",
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       "      <th>74392</th>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>576</td>\n",
       "      <td>M</td>\n",
       "      <td>60</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.59</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>718</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74393</th>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>576</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.03</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>688</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74394</th>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>576</td>\n",
       "      <td>F</td>\n",
       "      <td>83</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>189.67</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1507</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74395</th>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>576</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>143.02</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74396 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person  time gender  age    income  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef     0      F   75  100000.0   \n",
       "1      a03223e636434f42ac4c3df47e8bac43     0      0  118       0.0   \n",
       "2      e2127556f4f64592b11af22de27a7932     0      M   68   70000.0   \n",
       "3      68617ca6246f4fbc85e91a2a49552598     0      0  118       0.0   \n",
       "4      389bc3fa690240e798340f5a15918d5c     0      M   65   53000.0   \n",
       "...                                 ...   ...    ...  ...       ...   \n",
       "74391  d087c473b4d247ccb0abfef59ba12b0e   576      M   56   51000.0   \n",
       "74392  cb23b66c56f64b109d673d5e56574529   576      M   60  113000.0   \n",
       "74393  6d5f3a774f3d4714ab0c092238f3a1d7   576      F   45   54000.0   \n",
       "74394  9dc1421481194dcd9400aec7c9ae6366   576      F   83   50000.0   \n",
       "74395  e4052622e5ba45a8b96b59aba68cf068   576      F   62   82000.0   \n",
       "\n",
       "       difficulty  rewards_p  duration  is_mobile  is_web  ...  discount  \\\n",
       "0             5.0        5.0       7.0        1.0     1.0  ...       0.0   \n",
       "1            20.0        5.0      10.0        0.0     1.0  ...       1.0   \n",
       "2            10.0        2.0       7.0        1.0     1.0  ...       1.0   \n",
       "3            10.0       10.0       5.0        1.0     1.0  ...       0.0   \n",
       "4             5.0        5.0       5.0        1.0     1.0  ...       0.0   \n",
       "...           ...        ...       ...        ...     ...  ...       ...   \n",
       "74391        10.0       10.0       7.0        1.0     0.0  ...       0.0   \n",
       "74392        10.0        2.0       7.0        1.0     1.0  ...       1.0   \n",
       "74393         7.0        3.0       7.0        1.0     1.0  ...       1.0   \n",
       "74394        10.0       10.0       7.0        1.0     0.0  ...       0.0   \n",
       "74395         0.0        0.0       4.0        1.0     1.0  ...       0.0   \n",
       "\n",
       "       informational  cum_amount  abs_offers_completed   spend  \\\n",
       "0                0.0        0.00                   3.0  159.27   \n",
       "1                0.0        0.00                   0.0    4.65   \n",
       "2                0.0        0.00                   2.0   57.73   \n",
       "3                0.0        0.00                   0.0    0.24   \n",
       "4                0.0        0.00                   5.0   36.43   \n",
       "...              ...         ...                   ...     ...   \n",
       "74391            0.0       72.01                   4.0  107.56   \n",
       "74392            0.0      115.59                   1.0  115.59   \n",
       "74393            0.0       13.27                   0.0   20.03   \n",
       "74394            0.0      131.17                   3.0  189.67   \n",
       "74395            1.0      118.31                   2.0  143.02   \n",
       "\n",
       "       percent_offers_viewed  percent_offers_completed_x  abs_offers_viewed  \\\n",
       "0                 100.000000                   75.000000                4.0   \n",
       "1                  60.000000                    0.000000                3.0   \n",
       "2                  75.000000                   50.000000                3.0   \n",
       "3                  80.000000                    0.000000                4.0   \n",
       "4                 100.000000                   83.333333                6.0   \n",
       "...                      ...                         ...                ...   \n",
       "74391              83.333333                   66.666667                5.0   \n",
       "74392               0.000000                   33.333333                0.0   \n",
       "74393             100.000000                    0.000000                3.0   \n",
       "74394             100.000000                  100.000000                3.0   \n",
       "74395              50.000000                   50.000000                2.0   \n",
       "\n",
       "       member_since_x_days  responsive_customer  \n",
       "0                     1079                    1  \n",
       "1                      992                    0  \n",
       "2                      727                    0  \n",
       "3                      933                    0  \n",
       "4                      803                    1  \n",
       "...                    ...                  ...  \n",
       "74391                 1277                    0  \n",
       "74392                  718                    0  \n",
       "74393                  688                    0  \n",
       "74394                 1507                    1  \n",
       "74395                 1005                    0  \n",
       "\n",
       "[74396 rows x 22 columns]"
      ]
     },
     "execution_count": 1235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_multi_class = joblib.load('../data/interim/final_target_df_2104.joblib')\n",
    "new_multi_class.responsive_customer = new_df.responsive_customer.astype(int)\n",
    "new_multi_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_2(row):\n",
    "    if row['percent_offers_completed_x'] >=75 :\n",
    "        val = 1\n",
    "    elif row['percent_offers_completed_x'] <= 25:\n",
    "        val = 0\n",
    "    else:\n",
    "        val = 2\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1244,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_multi_class['label'] = new_multi_class.apply(f_2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['person', 'time', 'gender', 'age', 'income', 'difficulty', 'rewards_p',\n",
       "       'duration', 'is_mobile', 'is_web', 'is_social', 'bogo', 'discount',\n",
       "       'informational', 'cum_amount', 'spend', 'percent_offers_viewed',\n",
       "       'abs_offers_viewed', 'member_since_x_days', 'responsive_customer',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_multi_class.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1266,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_multi_class = new_multi_class.drop('percent_offers_completed_x', axis=1)\n",
    "new_multi_class = new_multi_class.drop('C', axis=1)\n",
    "new_multi_class = new_multi_class.drop('abs_offers_completed', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1268,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_multi_class = new_multi_class.drop('responsive_customer', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1269,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_multi_class.drop('label', axis=1)\n",
    "y = new_multi_class.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, 18])"
      ]
     },
     "execution_count": 1270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features_indices = np.where(X.dtypes != np.float)[0]\n",
    "categorical_features_indices "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1271,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False, \n",
    "                                                        random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1272,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pool = Pool(data=X_train, label=y_train, cat_features=categorical_features_indices)\n",
    "test_pool = Pool(data=X_test, label=y_test, cat_features=categorical_features_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1273,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = CatBoostClassifier(\n",
    "        iterations=1000,\n",
    "        loss_function='MultiClass',\n",
    "        custom_loss=['Accuracy'],\n",
    "        random_seed=42,\n",
    "        logging_level='Silent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    display: inline-block;\n",
       "    padding: 5px 10px 0 0;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart_active {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
       "    white-space: nowrap;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "#catboost-control-test {\n",
       "    margin-left: 11px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_label {\n",
       "    padding: 0 0 0 8px;\n",
       "    width: 200px;\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    cursor: pointer;\n",
       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__learn_hint {\n",
       "    top: 56px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__test_hint {\n",
       "    top: 82px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom {\n",
       "    padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time {\n",
       "    position: absolute;\n",
       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
       "    top: -3px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic {\n",
       "    border-top-style: dashed;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_test_pic {\n",
       "    border-top-style: solid;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie-value {\n",
       "    display: inline-block;\n",
       "    min-width: 30px;\n",
       "    margin-right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label .catboost-panel__serie_learn_pic {\n",
       "    padding-left: 4px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_names {\n",
       "    white-space: nowrap;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    width: 85px;\n",
       "    position: relative;\n",
       "    padding: 0 8px 0 0;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_iteration,\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    display: inline-block;\n",
       "    position: absolute;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_iteration {\n",
       "    top: 55px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    top: 80px;\n",
       "}\n",
       "\n",
       ".catboost-panel__control_slider {\n",
       "    width: 100px !important;\n",
       "    margin-left: 0;\n",
       "    position: relative;\n",
       "    display: inline-block !important;\n",
       "    top: 3px;\n",
       "}\n",
       "\n",
       ".catboost-panel__control_slidervalue {\n",
       "    width: 50px;\n",
       "    padding: 2px 3px;\n",
       "    margin-left: 4px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time_spend,\n",
       ".catboost-panel__serie_time_left {\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time_left {\n",
       "    margin-left: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_best_learn_value {\n",
       "    display: inline-block;\n",
       "}\n",
       ".catboost-panel__serie_test_pic,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_name,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_value,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_best_learn_value {\n",
       "    display: none;\n",
       "}\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_pic,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_name,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_value,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_best_test_value {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "/*\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_test_value,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_best_test_value {\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "321060e9814c42119725293ac5940e3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model2.fit(\n",
    "    train_pool,\n",
    "    eval_set= test_pool,\n",
    "#     logging_level='Verbose',  # you can uncomment this for text output\n",
    "    plot=True\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "preds_class = model2.predict(X_test)\n",
    "print(preds_class[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.1171709969639778'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.9994623655913979'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5392    1    0]\n",
      " [   1 4052    2]\n",
      " [   2    2 5428]]\n"
     ]
    }
   ],
   "source": [
    " display(F'Learning Rate set to: {model2.get_all_params()[\"learning_rate\"]}')\n",
    " display(F'Accuracy Score: {accuracy_score(y_test, preds_class)}')\n",
    " matrix = confusion_matrix(y_test, preds_class)\n",
    " print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1335,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier, Pool, cv\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1372,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_2(row):\n",
    "    \n",
    "    if row['percent_offers_completed'] >=50 :\n",
    "        val = 0\n",
    "    else:\n",
    "        val = 1\n",
    "        \n",
    "    return val\n",
    "\n",
    "def f_3(row):\n",
    "    \n",
    "    if row['percent_offers_completed'] >=66:\n",
    "        val = 0\n",
    "    elif row['percent_offers_completed'] <33:\n",
    "        val = 1\n",
    "    else:\n",
    "        val = 2\n",
    "        \n",
    "    return val\n",
    "\n",
    "def f_4(row):\n",
    "    \n",
    "    if row['percent_offers_completed'] >=75 :\n",
    "        val = 0\n",
    "    elif row['percent_offers_completed'] <25:\n",
    "        val = 1\n",
    "    elif row['percent_offers_completed'] >=25 and row['percent_offers_completed'] < 50:\n",
    "        val = 2\n",
    "    else:\n",
    "        val = 3\n",
    "        \n",
    "    return val\n",
    "\n",
    "def f_5(row):\n",
    "    \n",
    "    if row['percent_offers_completed'] >=80 :\n",
    "        val = 0\n",
    "    elif row['percent_offers_completed'] <20:\n",
    "        val = 1\n",
    "    elif row['percent_offers_completed'] >=20 and row['percent_offers_completed'] < 40:\n",
    "        val = 2\n",
    "    elif row['percent_offers_completed'] >=40 and row['percent_offers_completed'] < 60:\n",
    "        val = 3\n",
    "    else:\n",
    "        val = 4\n",
    "        \n",
    "    return val\n",
    "\n",
    "\n",
    "def load_dataset(name=None, default_path='../data/interim/', tag='latest'):\n",
    "    \n",
    "    '''\n",
    "    This function\n",
    "    - loads a joblib file into dataframe from default_path location\n",
    "    - It loads lastest created file from the location unless name is specified\n",
    "    '''\n",
    "    if name == None:\n",
    "        df = joblib.load(default_path + 'latest.joblib')\n",
    "    else:\n",
    "        df = joblib.load(default_path + name)\n",
    "        \n",
    "    return df\n",
    "\n",
    "def create_class_label(df, n_class):\n",
    "    \n",
    "    '''\n",
    "    This function\n",
    "    - create a label column in given dataframe based on value of n_class\n",
    "    - i.e. if n_class = 2, it create two labels based on following conditions\n",
    "           if percent_offers_completed >= 50 then label = 1\n",
    "           if percent_offers_completed < 50 then label = 2\n",
    "    - return a dataframe\n",
    "    '''\n",
    "    f_list = [f_2, f_3, f_4, f_5]\n",
    "       \n",
    "    df['label'] = df.apply(f_list[n_class-2], axis=1)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def feature_clean_up(df):\n",
    "    \n",
    "    '''\n",
    "    This function \n",
    "    - removes redudant features \n",
    "    - removes features from which labels were derived\n",
    "    '''\n",
    "    df = df.drop('percent_offers_completed', axis=1)\n",
    "    df = df.drop('abs_offers_completed', axis=1)\n",
    "    \n",
    "    return df\n",
    "    \n",
    "\n",
    "def train_test_model(n_class, custom_categories = False, categories_list = None, verbose=True, labels = None,):\n",
    "    \n",
    "    '''\n",
    "    This function \n",
    "     - loads the prepared dataset into dataframe\n",
    "     - creates a class labels as per 'n_class' value [max capped at 5]\n",
    "     - removes redudant features\n",
    "     - save a processed dataframe as joblib file \n",
    "     - create test and train split\n",
    "     - create catboost pool\n",
    "     - configures catboost model with default parameters for multiclass classifiers\n",
    "     - configures default catergorical attrributes unless customer catergories are provided \n",
    "     - train/fit a model with fixed number of iterations\n",
    "     - returns accuracy score, learning rate, number of iteration and n_class values in a list\n",
    "     \n",
    "    '''\n",
    "    \n",
    "    df = load_dataset()\n",
    "    \n",
    "    if n_class > 5:\n",
    "        n_class = 5\n",
    "    \n",
    "    if n_class < 2:\n",
    "        n_class = 2\n",
    "        \n",
    "    df = create_class_label(df, n_class)\n",
    "    df = feature_clean_up(df)\n",
    "    save_file(df, 'procssed_class_' + str(n_class) + '_latest.joblib', path='../data/processed')\n",
    "    \n",
    "    y = df.label\n",
    "    X = df.drop('label', axis=1)\n",
    "    \n",
    "    if custom_categories != True:\n",
    "        categorical_features_indices = np.where(X.dtypes != np.float)[0]\n",
    "    else:\n",
    "         # Assigns columns index location of categorical features\n",
    "        if categories_list != None:\n",
    "            categorical_features_indices = [X.columns.get_loc(i) for i in categories_list if i in X.columns]\n",
    "            \n",
    "    categorical_features_indices = np.where(X.dtypes != np.float)[0]\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False, \n",
    "                                                        random_state=21)\n",
    "    # Assigns weights to labels since these are unbalanced\n",
    "    weights = [df.label.value_counts().sum() / df.label.value_counts()[i] for i in \n",
    "               set(labels.values())]\n",
    "    \n",
    "    train_pool = Pool(data=X_train, label=y_train, cat_features=categorical_features_indices)\n",
    "    test_pool = Pool(data=X_test, label=y_test, cat_features=categorical_features_indices)\n",
    "    \n",
    "    model = CatBoostClassifier(\n",
    "        iterations= 4000,\n",
    "        loss_function='MultiClass',\n",
    "        early_stopping_rounds=50,\n",
    "        cat_features=categorical_features_indices,\n",
    "        class_weights= weights,\n",
    "        custom_loss=['Accuracy'],\n",
    "        logging_level='Silent')\n",
    "    \n",
    "    model = model.fit(train_pool,\n",
    "              eval_set=test_pool,\n",
    "              plot=True);\n",
    "    \n",
    "    preds_class = model.predict(X_test)\n",
    "    \n",
    "    if verbose:\n",
    "        \n",
    "        display(F'Learning Rate set to: {model.get_all_params()[\"learning_rate\"]}')\n",
    "        display(F'Accuracy Score: {accuracy_score(y_test, preds_class)}')\n",
    "        display(F'Weights: {weights}')\n",
    "        matrix = confusion_matrix(y_test, preds_class)\n",
    "        print('Confusion Matrix: ')\n",
    "        print(matrix)\n",
    "        \n",
    "    print('Starting the trainig with 3-Fold validation using Catboost CV:')\n",
    "\n",
    "    cv_params = model.get_params()\n",
    "    \n",
    "    cv_data = cv(train_pool,\n",
    "                 cv_params,\n",
    "                 iterations = 500,\n",
    "                 shuffle = False,\n",
    "                 early_stopping_rounds=50,\n",
    "                 plot=True)\n",
    "    \n",
    "    print(cv_data)\n",
    "    normal_test_acc = accuracy_score(y_test, preds_class)\n",
    "    \n",
    "    iterations = 500\n",
    "    return_list = []\n",
    "    #return_list.append(cv_validation_acc)\n",
    "    return_list.append(normal_test_acc)\n",
    "    return_list.append(iterations)\n",
    "    return_list.append(n_class)\n",
    "    \n",
    "    return return_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as ../data/processed/procssed_class_4_latest.joblib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    display: inline-block;\n",
       "    padding: 5px 10px 0 0;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
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       "\n",
       ".catboost-graph__chart_active {\n",
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       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
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       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
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       "\n",
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       "\n",
       ".catboost-panel__serie_label {\n",
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       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
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       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
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       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__learn_hint {\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
       ".catboost-panel__serie_time {\n",
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       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
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       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
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       "    margin-right: 5px;\n",
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       "\n",
       ".catboost-panel__serie_learn_pic {\n",
       "    border-top-style: dashed;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_test_pic {\n",
       "    border-top-style: solid;\n",
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       "\n",
       ".catboost-panel__serie-value {\n",
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       "    margin-right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label .catboost-panel__serie_learn_pic {\n",
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       "}\n",
       "\n",
       ".catboost-panel__serie_names {\n",
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       "}\n",
       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    width: 85px;\n",
       "    position: relative;\n",
       "    padding: 0 8px 0 0;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    top: 5px;\n",
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       "\n",
       ".catboost-panel__serie_iteration,\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    display: inline-block;\n",
       "    position: absolute;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_iteration {\n",
       "    top: 55px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    top: 80px;\n",
       "}\n",
       "\n",
       ".catboost-panel__control_slider {\n",
       "    width: 100px !important;\n",
       "    margin-left: 0;\n",
       "    position: relative;\n",
       "    display: inline-block !important;\n",
       "    top: 3px;\n",
       "}\n",
       "\n",
       ".catboost-panel__control_slidervalue {\n",
       "    width: 50px;\n",
       "    padding: 2px 3px;\n",
       "    margin-left: 4px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time_spend,\n",
       ".catboost-panel__serie_time_left {\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time_left {\n",
       "    margin-left: 10px;\n",
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       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_best_learn_value {\n",
       "    display: inline-block;\n",
       "}\n",
       ".catboost-panel__serie_test_pic,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_name,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_learn_value,\n",
       ".catboost-panel__series_learn_disabled .catboost-panel__serie_best_learn_value {\n",
       "    display: none;\n",
       "}\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_pic,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_name,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_test_value,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_best_test_value {\n",
       "    display: none;\n",
       "}\n",
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       "    width: 216px;\n",
       "}\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_learn_value,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_best_learn_value {\n",
       "    width: 216px;\n",
       "}\n",
       "*/\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie__test_hint,\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_best_iteration {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-panel__series_test_disabled.catboost-panel__series_learn_disabled .catboost-panel__serie_middle {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-panel__series_test_disabled .catboost-panel__serie_bottom {\n",
       "    display: none;\n",
       "}\n",
       "\n",
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n=t(\"./arrow_paths\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/cartesian/constants\"),i=t(\"../../lib/extend\").extendFlat;e.exports={_isLinkedToArray:\"annotation\",visible:{valType:\"boolean\",dflt:!0},text:{valType:\"string\"},textangle:{valType:\"angle\",dflt:0},font:i({},a,{}),width:{valType:\"number\",min:1,dflt:null},height:{valType:\"number\",min:1,dflt:null},opacity:{valType:\"number\",min:0,max:1,dflt:1},align:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"center\"},valign:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"],dflt:\"middle\"},bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\"},bordercolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\"},borderpad:{valType:\"number\",min:0,dflt:1},borderwidth:{valType:\"number\",min:0,dflt:1},showarrow:{valType:\"boolean\",dflt:!0},arrowcolor:{valType:\"color\"},arrowhead:{valType:\"integer\",min:0,max:n.length,dflt:1},arrowsize:{valType:\"number\",min:.3,dflt:1},arrowwidth:{valType:\"number\",min:.1},standoff:{valType:\"number\",min:0,dflt:0},ax:{valType:\"any\"},ay:{valType:\"any\"},axref:{valType:\"enumerated\",dflt:\"pixel\",values:[\"pixel\",o.idRegex.x.toString()]},ayref:{valType:\"enumerated\",dflt:\"pixel\",values:[\"pixel\",o.idRegex.y.toString()]},xref:{valType:\"enumerated\",values:[\"paper\",o.idRegex.x.toString()]},x:{valType:\"any\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"auto\"},xshift:{valType:\"number\",dflt:0},yref:{valType:\"enumerated\",values:[\"paper\",o.idRegex.y.toString()]},y:{valType:\"any\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"auto\"},yshift:{valType:\"number\",dflt:0},clicktoshow:{valType:\"enumerated\",values:[!1,\"onoff\",\"onout\"],dflt:!1},xclick:{valType:\"any\"},yclick:{valType:\"any\"},hovertext:{valType:\"string\"},hoverlabel:{bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\"},font:i({},a,{})},captureevents:{valType:\"boolean\"},_deprecated:{ref:{valType:\"string\"}}}},{\"../../lib/extend\":132,\"../../plots/cartesian/constants\":176,\"../../plots/font_attributes\":195,\"./arrow_paths\":15}],17:[function(t,e,r){\"use 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J=1/0;for(I=0;I<Y.length;I++)if((R=Y[I])&&R[0]&&R[0].trace&&R[0].trace.visible===!0&&(F=R[0].trace,[\"carpet\",\"contourcarpet\"].indexOf(F._module.name)===-1)){if(j=w.getSubplot(F),B=n.indexOf(j),q=D,U={cd:R,trace:F,xa:T[B],ya:L[B],name:t.data.length>1||F.hoverinfo.indexOf(\"name\")!==-1?F.name:void 0,index:!1,distance:Math.min(J,k.MAXDIST),color:y.defaultLine,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},d[j]&&(U.subplot=d[j]._subplot),X=G.length,\"array\"===q){var K=e[I];\"pointNumber\"in K?(U.index=K.pointNumber,q=\"closest\"):(q=\"\",\"xval\"in K&&(H=K.xval,q=\"x\"),\"yval\"in K&&(V=K.yval,q=q?\"closest\":\"y\"))}else H=P[B],V=E[B];if(F._module&&F._module.hoverPoints){var tt=F._module.hoverPoints(U,H,V,q);if(tt)for(var et,rt=0;rt<tt.length;rt++)et=tt[rt],f(et.x0)&&f(et.y0)&&G.push(l(et,D))}else h.log(\"Unrecognized trace type in hover:\",F);\"closest\"===D&&G.length>X&&(G.splice(0,X),J=G[0].distance)}if(0===G.length)return x.unhoverRaw(t,e);G.sort(function(t,e){return t.distance-e.distance});var nt=t._hoverdata,at=[];for(N=0;N<G.length;N++){var ot=G[N],it={data:ot.trace._input,fullData:ot.trace,curveNumber:ot.trace.index,pointNumber:ot.index};ot.trace._module.eventData?it=ot.trace._module.eventData(it,ot):(it.x=ot.xVal,it.y=ot.yVal,it.xaxis=ot.xa,it.yaxis=ot.ya,void 0!==ot.zLabelVal&&(it.z=ot.zLabelVal)),at.push(it)}if(t._hoverdata=at,c(t,e,nt)&&d._hasCartesian){s(G,{hovermode:D,fullLayout:d,container:d._hoverlayer,outerContainer:d._paperdiv})}var lt=\"y\"===D&&Y.length>1,st=y.combine(d.plot_bgcolor||y.background,d.paper_bgcolor),ct={hovermode:D,rotateLabels:lt,bgColor:st,container:d._hoverlayer,outerContainer:d._paperdiv,commonLabelOpts:d.hoverlabel},ut=a(G,ct);if(o(G,lt?\"xa\":\"ya\"),i(ut,lt),e.target&&e.target.tagName){var ft=_.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,at);v(u.select(e.target),ft?\"pointer\":\"\")}e.target&&c(t,e,nt)&&(nt&&t.emit(\"plotly_unhover\",{event:e,points:nt}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:T,yaxes:L,xvals:P,yvals:E}))}function a(t,e){var r,n,a=e.hovermode,o=e.rotateLabels,i=e.bgColor,l=e.container,s=e.outerContainer,c=e.commonLabelOpts||{},f=e.fontFamily||k.HOVERFONT,d=e.fontSize||k.HOVERFONTSIZE,h=t[0],p=h.xa,v=h.ya,x=\"y\"===a?\"yLabel\":\"xLabel\",b=h[x],_=(String(b)||\"\").split(\" \")[0],w=s.node().getBoundingClientRect(),A=w.top,T=w.width,L=w.height,C=h.distance<=k.MAXDIST&&(\"x\"===a||\"y\"===a);for(r=0;r<t.length;r++){n=t[r].trace.hoverinfo;var O=n.split(\"+\");if(O.indexOf(\"all\")===-1&&O.indexOf(a)===-1){C=!1;break}}var D=l.selectAll(\"g.axistext\").data(C?[0]:[]);D.enter().append(\"g\").classed(\"axistext\",!0),D.exit().remove(),D.each(function(){var e=u.select(this),r=e.selectAll(\"path\").data([0]),n=e.selectAll(\"text\").data([0]);r.enter().append(\"path\").style({fill:c.bgcolor||y.defaultLine,stroke:c.bordercolor||y.background,\"stroke-width\":\"1px\"}),n.enter().append(\"text\").call(m.font,c.font.family||f,c.font.size||d,c.font.color||y.background).attr(\"data-notex\",1),n.text(b).call(g.convertToTspans).call(m.setPosition,0,0).selectAll(\"tspan.line\").call(m.setPosition,0,0),e.attr(\"transform\",\"\");var o=n.node().getBoundingClientRect();if(\"x\"===a){n.attr(\"text-anchor\",\"middle\").call(m.setPosition,0,\"top\"===p.side?A-o.bottom-S-z:A-o.top+S+z).selectAll(\"tspan.line\").attr({x:n.attr(\"x\"),y:n.attr(\"y\")});var i=\"top\"===p.side?\"-\":\"\";r.attr(\"d\",\"M0,0L\"+S+\",\"+i+S+\"H\"+(z+o.width/2)+\"v\"+i+(2*z+o.height)+\"H-\"+(z+o.width/2)+\"V\"+i+S+\"H-\"+S+\"Z\"),e.attr(\"transform\",\"translate(\"+(p._offset+(h.x0+h.x1)/2)+\",\"+(v._offset+(\"top\"===p.side?0:v._length))+\")\")}else{n.attr(\"text-anchor\",\"right\"===v.side?\"start\":\"end\").call(m.setPosition,(\"right\"===v.side?1:-1)*(z+S),A-o.top-o.height/2).selectAll(\"tspan.line\").attr({x:n.attr(\"x\"),y:n.attr(\"y\")});var l=\"right\"===v.side?\"\":\"-\";r.attr(\"d\",\"M0,0L\"+l+S+\",\"+S+\"V\"+(z+o.height/2)+\"h\"+l+(2*z+o.width)+\"V-\"+(z+o.height/2)+\"H\"+l+S+\"V-\"+S+\"Z\"),e.attr(\"transform\",\"translate(\"+(p._offset+(\"right\"===v.side?p._length:0))+\",\"+(v._offset+(h.y0+h.y1)/2)+\")\")}t=t.filter(function(t){return void 0!==t.zLabelVal||(t[x]||\"\").split(\" \")[0]===_})});var P=l.selectAll(\"g.hovertext\").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya||\"\"].join(\",\")});return P.enter().append(\"g\").classed(\"hovertext\",!0).each(function(){var t=u.select(this);t.append(\"rect\").call(y.fill,y.addOpacity(i,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(m.font,f,d)}),P.exit().remove(),P.each(function(t){var e=u.select(this).attr(\"transform\",\"\"),r=\"\",n=\"\",l=y.opacity(t.color)?t.color:y.defaultLine,s=y.combine(l,i),c=t.borderColor||y.contrast(s);void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(r=g.plainText(t.name||\"\"),r.length>15&&(r=r.substr(0,12)+\"...\")),void 0!==t.extraText&&(n+=t.extraText),void 0!==t.zLabel?(void 0!==t.xLabel&&(n+=\"x: \"+t.xLabel+\"<br>\"),void 0!==t.yLabel&&(n+=\"y: \"+t.yLabel+\"<br>\"),n+=(n?\"z: \":\"\")+t.zLabel):C&&t[a+\"Label\"]===b?n=t[(\"x\"===a?\"y\":\"x\")+\"Label\"]||\"\":void 0===t.xLabel?void 0!==t.yLabel&&(n=t.yLabel):n=void 0===t.yLabel?t.xLabel:\"(\"+t.xLabel+\", \"+t.yLabel+\")\",t.text&&!Array.isArray(t.text)&&(n+=(n?\"<br>\":\"\")+t.text),\"\"===n&&(\"\"===r&&e.remove(),n=r);var h=e.select(\"text.nums\").call(m.font,t.fontFamily||f,t.fontSize||d,t.fontColor||c).call(m.setPosition,0,0).text(n).attr(\"data-notex\",1).call(g.convertToTspans);h.selectAll(\"tspan.line\").call(m.setPosition,0,0);var p=e.select(\"text.name\"),v=0;r&&r!==n?(p.call(m.font,t.fontFamily||f,t.fontSize||d,s).text(r).call(m.setPosition,0,0).attr(\"data-notex\",1).call(g.convertToTspans),p.selectAll(\"tspan.line\").call(m.setPosition,0,0),v=p.node().getBoundingClientRect().width+2*z):(p.remove(),e.select(\"rect\").remove()),e.select(\"path\").style({fill:s,stroke:c});var x,_,w=h.node().getBoundingClientRect(),k=t.xa._offset+(t.x0+t.x1)/2,O=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),P=Math.abs(t.y1-t.y0),E=w.width+S+z+v;t.ty0=A-w.top,t.bx=w.width+2*z,t.by=w.height+2*z,t.anchor=\"start\",t.txwidth=w.width,t.tx2width=v,t.offset=0,o?(t.pos=k,x=O+P/2+E<=L,_=O-P/2-E>=0,\"top\"!==t.idealAlign&&x||!_?x?(O+=P/2,t.anchor=\"start\"):t.anchor=\"middle\":(O-=P/2,t.anchor=\"end\")):(t.pos=O,x=k+D/2+E<=T,_=k-D/2-E>=0,\"left\"!==t.idealAlign&&x||!_?x?(k+=D/2,t.anchor=\"start\"):t.anchor=\"middle\":(k-=D/2,t.anchor=\"end\")),h.attr(\"text-anchor\",t.anchor),v&&p.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",\"translate(\"+k+\",\"+O+\")\"+(o?\"rotate(\"+M+\")\":\"\"))}),P}function o(t,e){function r(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;n=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;n=!1}if(n){var c=0;for(i=0;i<t.length;i++)s=t[i],s.pos+s.dp+s.size>e.pmax&&c++;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos>e.pmax-1&&(s.del=!0,c--);for(i=0;i<t.length&&!(c<=0);i++)if(s=t[i],s.pos<e.pmin+1)for(s.del=!0,c--,o=2*s.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos+s.dp+s.size>e.pmax&&(s.del=!0,c--)}}}for(var n,a,o,i,l,s,c,u=0,f=t.map(function(t,r){var n=t[e];return[{i:r,dp:0,pos:t.pos,posref:t.posref,size:t.by*(\"x\"===n._id.charAt(0)?T:1)/2,pmin:n._offset,pmax:n._offset+n._length}]}).sort(function(t,e){return t[0].posref-e[0].posref});!n&&u<=t.length;){for(u++,n=!0,i=0;i<f.length-1;){var d=f[i],h=f[i+1],p=d[d.length-1],g=h[0];if((a=p.pos+p.dp+p.size-g.pos-g.dp+g.size)>.01&&p.pmin===g.pmin&&p.pmax===g.pmax){for(l=h.length-1;l>=0;l--)h[l].dp+=a;for(d.push.apply(d,h),f.splice(i+1,1),c=0,l=d.length-1;l>=0;l--)c+=d[l].dp;for(o=c/d.length,l=d.length-1;l>=0;l--)d[l].dp-=o;n=!1}else i++}f.forEach(r)}for(i=f.length-1;i>=0;i--){var v=f[i];for(l=v.length-1;l>=0;l--){var m=v[l],y=t[m.i];y.offset=m.dp,y.del=m.del}}}function i(t,e){t.each(function(t){var r=u.select(this);if(t.del)return void r.remove();var n=\"end\"===t.anchor?-1:1,a=r.select(\"text.nums\"),o={start:1,end:-1,middle:0}[t.anchor],i=o*(S+z),l=i+o*(t.txwidth+z),s=0,c=t.offset;\"middle\"===t.anchor&&(i-=t.tx2width/2,l-=t.tx2width/2),e&&(c*=-C,s=t.offset*L),r.select(\"path\").attr(\"d\",\"middle\"===t.anchor?\"M-\"+t.bx/2+\",-\"+t.by/2+\"h\"+t.bx+\"v\"+t.by+\"h-\"+t.bx+\"Z\":\"M0,0L\"+(n*S+s)+\",\"+(S+c)+\"v\"+(t.by/2-S)+\"h\"+n*t.bx+\"v-\"+t.by+\"H\"+(n*S+s)+\"V\"+(c-S)+\"Z\"),a.call(m.setPosition,i+s,c+t.ty0-t.by/2+z).selectAll(\"tspan.line\").attr({x:a.attr(\"x\"),y:a.attr(\"y\")}),t.tx2width&&(r.select(\"text.name, text.name tspan.line\").call(m.setPosition,l+o*z+s,c+t.ty0-t.by/2+z),r.select(\"rect\").call(m.setRect,l+(o-1)*t.tx2width/2+s,c-t.by/2-1,t.tx2width,t.by+2))})}function l(t,e){function r(e,r,i){var l;if(o[r])l=o[r];else if(a[r]){var s=a[r];Array.isArray(s)&&Array.isArray(s[t.index[0]])&&(l=s[t.index[0]][t.index[1]])}else l=h.nestedProperty(n,i).get();l&&(t[e]=l)}var n=t.trace||{},a=t.cd[0],o=t.cd[t.index]||{};t.posref=\"y\"===e?(t.x0+t.x1)/2:(t.y0+t.y1)/2,t.x0=h.constrain(t.x0,0,t.xa._length),t.x1=h.constrain(t.x1,0,t.xa._length),t.y0=h.constrain(t.y0,0,t.ya._length),t.y1=h.constrain(t.y1,0,t.ya._length);var i;if(void 0!==t.xLabelVal){i=\"log\"===t.xa.type&&t.xLabelVal<=0;var l=b.tickText(t.xa,t.xa.c2l(i?-t.xLabelVal:t.xLabelVal),\"hover\");i?0===t.xLabelVal?t.xLabel=\"0\":t.xLabel=\"-\"+l.text:t.xLabel=l.text,t.xVal=t.xa.c2d(t.xLabelVal)}if(void 0!==t.yLabelVal){i=\"log\"===t.ya.type&&t.yLabelVal<=0;var s=b.tickText(t.ya,t.ya.c2l(i?-t.yLabelVal:t.yLabelVal),\"hover\");i?0===t.yLabelVal?t.yLabel=\"0\":t.yLabel=\"-\"+s.text:t.yLabel=s.text,t.yVal=t.ya.c2d(t.yLabelVal)}if(void 0!==t.zLabelVal&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||\"log\"===t.xa.type&&t.xerr<=0)){var c=b.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+c+\" / -\"+b.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" \\xb1 \"+c,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||\"log\"===t.ya.type&&t.yerr<=0)){var u=b.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+u+\" / -\"+b.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" \\xb1 \"+u,\"y\"===e&&(t.distance+=1)}var f=t.trace.hoverinfo;return\"all\"!==f&&(f=f.split(\"+\"),f.indexOf(\"x\")===-1&&(t.xLabel=void 0),f.indexOf(\"y\")===-1&&(t.yLabel=void 0),f.indexOf(\"z\")===-1&&(t.zLabel=void 0),f.indexOf(\"text\")===-1&&(t.text=void 0),f.indexOf(\"name\")===-1&&(t.name=void 0)),r(\"color\",\"hbg\",\"hoverlabel.bgcolor\"),r(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),r(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),r(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),r(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),t}function s(t,e){var r=e.hovermode,n=e.container,a=t[0],o=a.xa,i=a.ya,l=o.showspikes,s=i.showspikes;if(n.selectAll(\".spikeline\").remove(),\"closest\"===r&&(l||s)){var c=e.fullLayout,u=o._offset+(a.x0+a.x1)/2,f=i._offset+(a.y0+a.y1)/2,h=y.combine(c.plot_bgcolor,c.paper_bgcolor),p=d.readability(a.color,h)<1.5?y.contrast(h):a.color;if(s){var g=i.spikemode,v=i.spikethickness,x=i.spikecolor||p,b=i._boundingBox,_=(b.left+b.right)/2<u?b.right:b.left;if(g.indexOf(\"toaxis\")!==-1||g.indexOf(\"across\")!==-1){var w=_,k=u;g.indexOf(\"across\")!==-1&&(w=i._counterSpan[0],k=i._counterSpan[1]),n.append(\"line\").attr({x1:w,x2:k,y1:f,y2:f,\"stroke-width\":v+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0),n.append(\"line\").attr({x1:w,x2:k,y1:f,y2:f,\"stroke-width\":v,stroke:x,\"stroke-dasharray\":m.dashStyle(i.spikedash,v)}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}g.indexOf(\"marker\")!==-1&&n.append(\"circle\").attr({cx:_+(\"right\"!==i.side?v:-v),cy:f,r:v,fill:x}).classed(\"spikeline\",!0)}if(l){var M=o.spikemode,A=o.spikethickness,T=o.spikecolor||p,L=o._boundingBox,C=(L.top+L.bottom)/2<f?L.bottom:L.top;if(M.indexOf(\"toaxis\")!==-1||M.indexOf(\"across\")!==-1){var S=C,z=f;M.indexOf(\"across\")!==-1&&(S=o._counterSpan[0],z=o._counterSpan[1]),n.append(\"line\").attr({x1:u,x2:u,y1:S,y2:z,\"stroke-width\":A+2,stroke:h}).classed(\"spikeline\",!0).classed(\"crisp\",!0),n.append(\"line\").attr({x1:u,x2:u,y1:S,y2:z,\"stroke-width\":A,stroke:T,\"stroke-dasharray\":m.dashStyle(o.spikedash,A)}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}M.indexOf(\"marker\")!==-1&&n.append(\"circle\").attr({cx:u,cy:C-(\"top\"!==o.side?A:-A),r:A,fill:T}).classed(\"spikeline\",!0)}}}function c(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var a=r[n],o=t._hoverdata[n];if(a.curveNumber!==o.curveNumber||String(a.pointNumber)!==String(o.pointNumber))return!0}return!1}var u=t(\"d3\"),f=t(\"fast-isnumeric\"),d=t(\"tinycolor2\"),h=t(\"../../lib\"),p=t(\"../../lib/events\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../../lib/override_cursor\"),m=t(\"../drawing\"),y=t(\"../color\"),x=t(\"../dragelement\"),b=t(\"../../plots/cartesian/axes\"),_=t(\"../../registry\"),w=t(\"./helpers\"),k=t(\"./constants\"),M=k.YANGLE,A=Math.PI*M/180,T=1/Math.sin(A),L=Math.cos(A),C=Math.sin(A),S=k.HOVERARROWSIZE,z=k.HOVERTEXTPAD;r.hover=function(t,e,r){if(\"string\"==typeof t&&(t=document.getElementById(t)),void 0===t._lastHoverTime&&(t._lastHoverTime=0),void 0!==t._hoverTimer&&(clearTimeout(t._hoverTimer),t._hoverTimer=void 0),Date.now()>t._lastHoverTime+k.HOVERMINTIME)return n(t,e,r),void(t._lastHoverTime=Date.now());t._hoverTimer=setTimeout(function(){n(t,e,r),t._lastHoverTime=Date.now(),t._hoverTimer=void 0},k.HOVERMINTIME)},r.loneHover=function(t,e){var r={color:t.color||y.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,trace:{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0},n=u.select(e.container),o=e.outerContainer?u.select(e.outerContainer):n,l={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||y.background,container:n,outerContainer:o},s=a([r],l);return i(s,l.rotateLabels),s.node()}},{\"../../lib\":136,\"../../lib/events\":131,\"../../lib/override_cursor\":145,\"../../lib/svg_text_utils\":153,\"../../plots/cartesian/axes\":171,\"../../registry\":206,\"../color\":25,\"../dragelement\":46,\"../drawing\":49,\"./constants\":61,\"./helpers\":63,d3:7,\"fast-isnumeric\":10,tinycolor2:13}],65:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e,r,a){a=a||{},r(\"hoverlabel.bgcolor\",a.bgcolor),r(\"hoverlabel.bordercolor\",a.bordercolor),n.coerceFont(r,\"hoverlabel.font\",a.font)}},{\"../../lib\":136}],66:[function(t,e,r){\"use strict\";function n(t){var e=i.isD3Selection(t)?t:o.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()}function a(t,e,r){var n=t.hoverlabel||{},a=i.nestedProperty(n,r).get();return Array.isArray(a)?Array.isArray(e)&&Array.isArray(a[e[0]])?a[e[0]][e[1]]:a[e]:a}var 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n=o({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach(function(t,r){i.util.translator.apply(null,t.concat(e))}),e||delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",n.dotVisible===!0?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var a=i.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var n=a.indexOf(t.geometry);n!=-1&&(r.data[e].groupId=n)})}if(t.layout){var l=o({},t.layout);if([[l,[\"plot_bgcolor\"],[\"backgroundColor\"]],[l,[\"showlegend\"],[\"showLegend\"]],[l,[\"radialaxis\"],[\"radialAxis\"]],[l,[\"angularaxis\"],[\"angularAxis\"]],[l.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[l.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[l.angularaxis,[\"nticks\"],[\"ticksCount\"]],[l.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.angularaxis,[\"range\"],[\"domain\"]],[l.angularaxis,[\"endpadding\"],[\"endPadding\"]],[l.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[l.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.radialaxis,[\"range\"],[\"domain\"]],[l.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[l.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[l.angularAxis,[\"nticks\"],[\"ticksCount\"]],[l.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.angularAxis,[\"range\"],[\"domain\"]],[l.angularAxis,[\"endpadding\"],[\"endPadding\"]],[l.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[l.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[l.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[l.radialAxis,[\"range\"],[\"domain\"]],[l.font,[\"outlinecolor\"],[\"outlineColor\"]],[l.legend,[\"traceorder\"],[\"reverseOrder\"]],[l,[\"labeloffset\"],[\"labelOffset\"]],[l,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach(function(t,r){i.util.translator.apply(null,t.concat(e))}),e?(void 0!==l.tickLength&&(l.angularaxis.ticklen=l.tickLength,delete l.tickLength),l.tickColor&&(l.angularaxis.tickcolor=l.tickColor,delete l.tickColor)):(l.angularAxis&&void 0!==l.angularAxis.ticklen&&(l.tickLength=l.angularAxis.ticklen),l.angularAxis&&void 0!==l.angularAxis.tickcolor&&(l.tickColor=l.angularAxis.tickcolor)),l.legend&&\"boolean\"!=typeof l.legend.reverseOrder&&(l.legend.reverseOrder=\"normal\"!=l.legend.reverseOrder),l.legend&&\"boolean\"==typeof l.legend.traceorder&&(l.legend.traceorder=l.legend.traceorder?\"reversed\":\"normal\",delete l.legend.reverseOrder),l.margin&&void 0!==l.margin.t){var s=[\"t\",\"r\",\"b\",\"l\",\"pad\"],c=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],u={};n.entries(l.margin).forEach(function(t,e){u[c[s.indexOf(t.key)]]=t.value}),l.margin=u}e&&(delete l.needsEndSpacing,delete l.minorTickColor,delete l.minorTicks,delete l.angularaxis.ticksCount,delete l.angularaxis.ticksCount,delete l.angularaxis.ticksStep,delete l.angularaxis.rewriteTicks,delete l.angularaxis.nticks,delete l.radialaxis.ticksCount,delete l.radialaxis.ticksCount,delete l.radialaxis.ticksStep,delete l.radialaxis.rewriteTicks,delete l.radialaxis.nticks),r.layout=l}return r},t}},{\"../../lib\":136,d3:7}],204:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"../../lib\"),o=t(\"../../components/color\"),i=t(\"./micropolar\"),l=t(\"./undo_manager\"),s=a.extendDeepAll,c=e.exports={};c.framework=function(t){function e(e,a){return a&&(f=a),n.select(n.select(f).node().parentNode).selectAll(\".svg-container>*:not(.chart-root)\").remove(),r=r?s(r,e):e,o||(o=i.Axis()),u=i.adapter.plotly().convert(r),o.config(u).render(f),t.data=r.data,t.layout=r.layout,c.fillLayout(t),r}var r,a,o,u,f,d=new l;return e.isPolar=!0,e.svg=function(){return o.svg()},e.getConfig=function(){return r},e.getLiveConfig=function(){return i.adapter.plotly().convert(o.getLiveConfig(),!0)},e.getLiveScales=function(){return{t:o.angularScale(),r:o.radialScale()}},e.setUndoPoint=function(){var t=this,e=i.util.cloneJson(r);!function(e,r){d.add({undo:function(){r&&t(r)},redo:function(){t(e)}})}(e,a),a=i.util.cloneJson(e)},e.undo=function(){d.undo()},e.redo=function(){d.redo()},e},c.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),a=t.framework&&t.framework.svg&&t.framework.svg(),i={width:800,height:600,paper_bgcolor:o.background,_container:e,_paperdiv:r,_paper:a};t._fullLayout=s(i,t.layout)}},{\"../../components/color\":25,\"../../lib\":136,\"./micropolar\":203,\"./undo_manager\":205,d3:7}],205:[function(t,e,r){\"use strict\";e.exports=function(){function t(t,e){return t?(a=!0,t[e](),a=!1,this):this}var e,r=[],n=-1,a=!1;return{add:function(t){return a?this:(r.splice(n+1,r.length-n),r.push(t),n=r.length-1,this)},setCallback:function(t){e=t},undo:function(){var a=r[n];return a?(t(a,\"undo\"),n-=1,e&&e(a.undo),this):this},redo:function(){var a=r[n+1];return 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Array(h.length),r=0;r<h.length;r++)p[r]=String(e.label0+r*e.dlabel);for(r=0;r<h.length;r++)s=h[r],a(s)&&((s=+s)<0||(c=p[r],void 0!==c&&\"\"!==c||(c=r),c=String(c),void 0===y[c]&&(y[c]=!0,u=o(e.marker.colors[r]),u.isValid()?(u=i.addOpacity(u,u.getAlpha()),m[c]||(m[c]=u)):m[c]?u=m[c]:(u=!1,x=!0),f=_.indexOf(c)!==-1,f||(b+=s),g.push({v:s,label:c,color:u,i:r,hidden:f}))));if(e.sort&&g.sort(function(t,e){return e.v-t.v}),x)for(r=0;r<g.length;r++)d=g[r],d.color===!1&&(m[d.label]=d.color=n(v._piedefaultcolorcount),v._piedefaultcolorcount++);if(g[0]&&(g[0].vTotal=b),e.textinfo&&\"none\"!==e.textinfo){var w,k=e.textinfo.indexOf(\"label\")!==-1,M=e.textinfo.indexOf(\"text\")!==-1,A=e.textinfo.indexOf(\"value\")!==-1,T=e.textinfo.indexOf(\"percent\")!==-1,L=v.separators;for(r=0;r<g.length;r++)d=g[r],w=k?[d.label]:[],M&&e.text[d.i]&&w.push(e.text[d.i]),A&&w.push(l.formatPieValue(d.v,L)),T&&w.push(l.formatPiePercent(d.v/b,L)),d.text=w.join(\"<br>\")}return g};var s},{\"../../components/color\":25,\"./helpers\":232,\"fast-isnumeric\":10,tinycolor2:13}],231:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./attributes\");e.exports=function(t,e,r,o){function i(r,o){return n.coerce(t,e,a,r,o)}var l=n.coerceFont,s=i(\"values\");if(!Array.isArray(s)||!s.length)return void(e.visible=!1);var c=i(\"labels\");Array.isArray(c)||(i(\"label0\"),i(\"dlabel\")),i(\"marker.line.width\")&&i(\"marker.line.color\");var u=i(\"marker.colors\");Array.isArray(u)||(e.marker.colors=[]),i(\"scalegroup\");var f=i(\"text\"),d=i(\"textinfo\",Array.isArray(f)?\"text+percent\":\"percent\");if(i(\"hovertext\"),i(\"hoverinfo\",1===o._dataLength?\"label+text+value+percent\":void 0),d&&\"none\"!==d){var h=i(\"textposition\"),p=Array.isArray(h)||\"auto\"===h,g=p||\"inside\"===h,v=p||\"outside\"===h;if(g||v){var m=l(i,\"textfont\",o.font);g&&l(i,\"insidetextfont\",m),v&&l(i,\"outsidetextfont\",m)}}i(\"domain.x\"),i(\"domain.y\"),i(\"hole\"),i(\"sort\"),i(\"direction\"),i(\"rotation\"),i(\"pull\")}},{\"../../lib\":136,\"./attributes\":228}],232:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return r.lastIndexOf(\".\")!==-1&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return r.lastIndexOf(\".\")!==-1&&(r=r.replace(/[.]?0+$/,\"\")),n.numSeparate(r,e)}},{\"../../lib\":136}],233:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 0,idealAlign:e.pxmid[0]<0?\"left\":\"right\",color:e.hbg||y.bgcolor||e.color,borderColor:e.hbc||y.bordercolor,fontFamily:e.htf||y.font.family,fontSize:e.hts||y.font.size,fontColor:e.htc||y.font.color},{container:o._hoverlayer.node(),outerContainer:o._paper.node()}),f.hover(t,n,\"pie\"),T=!0}function l(e){e.originalEvent=u.event,t.emit(\"plotly_unhover\",{event:u.event,points:[e]}),T&&(f.loneUnhover(r._hoverlayer.node()),T=!1)}function d(){t._hoverdata=[e],t._hoverdata.trace=c.trace,f.click(t,u.event)}function x(t,r,n,a){return\"a\"+a*c.r+\",\"+a*_+\" \"+y+\" \"+e.largeArc+(n?\" 1 \":\" 0 \")+a*(r[0]-t[0])+\",\"+a*(r[1]-t[1])}if(e.hidden)return void u.select(this).selectAll(\"path,g\").remove();s[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else 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t[0][0]===e[0]&&t[0][1]===e[1]?s.smoothclosed(t.slice(1),L.smoothing):s.smoothopen(t,L.smoothing)}:function(t){return\"M\"+t.join(\"L\")},j=function(t){return F(t.reverse())},G=f(n,{xaxis:M,yaxis:A,connectGaps:T.connectgaps,baseTolerance:Math.max(L.width||1,3)/4,linear:\"linear\"===L.shape,simplify:L.simplify}),V=T._polygons=new Array(G.length),w=0;w<G.length;w++)T._polygons[w]=h(G[w]);G.length&&(B=G[0][0],q=G[G.length-1],H=q[q.length-1]),Y=G.filter(function(t){return t.length>1}),Z=function(t){return function(e){if(N=R(e),I=j(e),U?O?(U+=\"L\"+N.substr(1),X=I+\"L\"+X.substr(1)):(U+=\"Z\"+N,X=I+\"Z\"+X):(U=N,X=I),u.hasLines(T)&&e.length>1){var r=i.select(this);if(r.datum(n),t)g(r.style(\"opacity\",0).attr(\"d\",N).call(s.lineGroupStyle)).style(\"opacity\",1);else{var a=g(r);a.attr(\"d\",N),s.singleLineStyle(n,a)}}}}}var W=C.selectAll(\".js-line\").data(Y);g(W.exit()).style(\"opacity\",0).remove(),W.each(Z(!1)),W.enter().append(\"path\").classed(\"js-line\",!0).style(\"vector-effect\",\"non-scaling-stroke\").call(s.lineGroupStyle).each(Z(!0)),G.length&&(S?B&&H&&(O?(\"y\"===O?B[1]=H[1]=A.c2p(0,!0):\"x\"===O&&(B[0]=H[0]=M.c2p(0,!0)),g(S).attr(\"d\",\"M\"+H+\"L\"+B+\"L\"+U.substr(1))):g(S).attr(\"d\",U+\"Z\")):\"tonext\"===T.fill.substr(0,6)&&U&&D&&(\"tonext\"===T.fill?g(z).attr(\"d\",U+\"Z\"+D+\"Z\"):g(z).attr(\"d\",U+\"L\"+D.substr(1)+\"Z\"),T._polygons=T._polygons.concat(P)),T._prevRevpath=X,T._prevPolygons=V);var $=C.selectAll(\".points\");_=$.data([n]),$.each(b),_.enter().append(\"g\").classed(\"points\",!0).each(b),_.exit().remove()}}function o(t,e,r,n,a){var o=r.xaxis,s=r.yaxis,c=i.extent(l.simpleMap(o.range,o.r2c)),f=i.extent(l.simpleMap(s.range,s.r2c)),d=n[0].trace;if(u.hasMarkers(d)){var h=d.marker.maxdisplayed;if(0!==h){var p=n.filter(function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b6d413d137941e986c233c764865e50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.06899800151586533'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.9991263440860215'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Weights: [3.657081059824018, 3.183942480527262, 5.74886021173016, 4.192268680265975]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4049    1    0    5]\n",
      " [   0 4629    0    4]\n",
      " [   0    0 2562    0]\n",
      " [   2    1    0 3627]]\n",
      "Starting the trainig with 3-Fold validation using Catboost CV:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
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       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
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       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart_active {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
       "    white-space: nowrap;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "#catboost-control-test {\n",
       "    margin-left: 11px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_label {\n",
       "    padding: 0 0 0 8px;\n",
       "    width: 200px;\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    cursor: pointer;\n",
       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__learn_hint {\n",
       "    top: 56px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__test_hint {\n",
       "    top: 82px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom {\n",
       "    padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time {\n",
       "    position: absolute;\n",
       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
       "    top: -3px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic {\n",
       "    border-top-style: dashed;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_test_pic {\n",
       "    border-top-style: solid;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie-value {\n",
       "    display: inline-block;\n",
       "    min-width: 30px;\n",
       "    margin-right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label .catboost-panel__serie_learn_pic {\n",
       "    padding-left: 4px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_names {\n",
       "    white-space: nowrap;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    width: 85px;\n",
       "    position: relative;\n",
       "    padding: 0 8px 0 0;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_iteration,\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    display: inline-block;\n",
       "    position: absolute;\n",
       "    box-sizing: content-box;\n",
       "    overflow: hidden;\n",
       "    right: 2px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_iteration {\n",
       "    top: 55px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_best_iteration {\n",
       "    top: 80px;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3508a8c3ff3e407aab90f01b37630839",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     iterations  test-MultiClass-mean  test-MultiClass-std  \\\n",
      "0             0              1.303106             0.002397   \n",
      "1             1              1.242430             0.027030   \n",
      "2             2              1.172560             0.026656   \n",
      "3             3              1.110611             0.025324   \n",
      "4             4              1.064474             0.024380   \n",
      "..          ...                   ...                  ...   \n",
      "495         495              0.030593             0.005727   \n",
      "496         496              0.030569             0.005693   \n",
      "497         497              0.030568             0.005687   \n",
      "498         498              0.030568             0.005688   \n",
      "499         499              0.030543             0.005723   \n",
      "\n",
      "     train-MultiClass-mean  train-MultiClass-std  \\\n",
      "0                 1.327776              0.001281   \n",
      "1                 1.277489              0.003984   \n",
      "2                 1.231891              0.004419   \n",
      "3                 1.191004              0.005771   \n",
      "4                 1.153434              0.003072   \n",
      "..                     ...                   ...   \n",
      "495               0.364281              0.000710   \n",
      "496               0.364179              0.000736   \n",
      "497               0.364149              0.000724   \n",
      "498               0.364109              0.000728   \n",
      "499               0.364053              0.000703   \n",
      "\n",
      "     test-Accuracy:use_weights=true-mean  test-Accuracy:use_weights=true-std  \\\n",
      "0                               0.973346                            0.010377   \n",
      "1                               0.988112                            0.003034   \n",
      "2                               0.989728                            0.004229   \n",
      "3                               0.989804                            0.004271   \n",
      "4                               0.989412                            0.003268   \n",
      "..                                   ...                                 ...   \n",
      "495                             0.991185                            0.003050   \n",
      "496                             0.991185                            0.003009   \n",
      "497                             0.991227                            0.003048   \n",
      "498                             0.991245                            0.003064   \n",
      "499                             0.991278                            0.003046   \n",
      "\n",
      "     train-Accuracy:use_weights=true-mean  \\\n",
      "0                                0.806709   \n",
      "1                                0.812316   \n",
      "2                                0.811387   \n",
      "3                                0.812833   \n",
      "4                                0.813004   \n",
      "..                                    ...   \n",
      "495                              0.839116   \n",
      "496                              0.839144   \n",
      "497                              0.839143   \n",
      "498                              0.839200   \n",
      "499                              0.839204   \n",
      "\n",
      "     train-Accuracy:use_weights=true-std  \\\n",
      "0                               0.006406   \n",
      "1                               0.001924   \n",
      "2                               0.000404   \n",
      "3                               0.005734   \n",
      "4                               0.006585   \n",
      "..                                   ...   \n",
      "495                             0.000355   \n",
      "496                             0.000360   \n",
      "497                             0.000342   \n",
      "498                             0.000290   \n",
      "499                             0.000324   \n",
      "\n",
      "     test-Accuracy:use_weights=false-mean  \\\n",
      "0                                0.970580   \n",
      "1                                0.988406   \n",
      "2                                0.990524   \n",
      "3                                0.990591   \n",
      "4                                0.990053   \n",
      "..                                    ...   \n",
      "495                              0.991515   \n",
      "496                              0.991515   \n",
      "497                              0.991548   \n",
      "498                              0.991565   \n",
      "499                              0.991599   \n",
      "\n",
      "     test-Accuracy:use_weights=false-std  \\\n",
      "0                               0.013853   \n",
      "1                               0.002141   \n",
      "2                               0.003748   \n",
      "3                               0.003793   \n",
      "4                               0.002876   \n",
      "..                                   ...   \n",
      "495                             0.002743   \n",
      "496                             0.002698   \n",
      "497                             0.002731   \n",
      "498                             0.002747   \n",
      "499                             0.002731   \n",
      "\n",
      "     train-Accuracy:use_weights=false-mean  \\\n",
      "0                                 0.817444   \n",
      "1                                 0.827307   \n",
      "2                                 0.831776   \n",
      "3                                 0.833759   \n",
      "4                                 0.834792   \n",
      "..                                     ...   \n",
      "495                               0.848856   \n",
      "496                               0.848873   \n",
      "497                               0.848873   \n",
      "498                               0.848906   \n",
      "499                               0.848915   \n",
      "\n",
      "     train-Accuracy:use_weights=false-std  \n",
      "0                                0.011379  \n",
      "1                                0.003976  \n",
      "2                                0.003889  \n",
      "3                                0.001548  \n",
      "4                                0.003347  \n",
      "..                                    ...  \n",
      "495                              0.000255  \n",
      "496                              0.000242  \n",
      "497                              0.000238  \n",
      "498                              0.000215  \n",
      "499                              0.000244  \n",
      "\n",
      "[500 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "separation = {'responsive': 3,\n",
    "              'very_responsive': 0,\n",
    "              'moderately_responsive': 2,\n",
    "              'unresponsive':1\n",
    "             }\n",
    "\n",
    "results = train_test_model(n_class=4, labels=separation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1361,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_4 = results\n",
    "results_4_train_accuracy_max = 0.839204\n",
    "results_4_test_accuracy_max = 0.991278"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as ../data/processed/procssed_class_5_latest.joblib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    display: inline-block;\n",
       "    padding: 5px 10px 0 0;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart_active {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
       "    white-space: nowrap;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "#catboost-control-test {\n",
       "    margin-left: 11px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_label {\n",
       "    padding: 0 0 0 8px;\n",
       "    width: 200px;\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
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       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__learn_hint {\n",
       "    top: 56px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie__test_hint {\n",
       "    top: 82px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom {\n",
       "    padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time {\n",
       "    position: absolute;\n",
       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
       "    top: -3px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic {\n",
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       "\n",
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       "    border-top-style: solid;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie-value {\n",
       "    display: inline-block;\n",
       "    min-width: 30px;\n",
       "    margin-right: 2px;\n",
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       "\n",
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       "\n",
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       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    width: 85px;\n",
       "    position: relative;\n",
       "    padding: 0 8px 0 0;\n",
       "    box-sizing: content-box;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72e7a5fc62af47c98dbc812675daa625",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.06899800151586533'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.999260752688172'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Weights: [4.666373957222605, 3.960183115085702, 5.882966946069904, 6.046980411281801, 5.054762875390678]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3159    1    0    4    0]\n",
      " [   1 3757    0    2    0]\n",
      " [   0    0 2483    0    0]\n",
      " [   2    1    0 2472    0]\n",
      " [   0    0    0    0 2998]]\n",
      "Starting the trainig with 3-Fold validation using Catboost CV:\n"
     ]
    },
    {
     "data": {
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       "    display: none !important;\n",
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       "\n",
       ".catboost {\n",
       "    position: relative;\n",
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       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
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       "\n",
       ".catboost-panel__controls {\n",
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       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
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       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
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       "    height: 1px;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9283d76ac90c4110a564d9e790b50127",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     iterations  test-MultiClass-mean  test-MultiClass-std  \\\n",
      "0             0              1.530552             0.049432   \n",
      "1             1              1.498799             0.054725   \n",
      "2             2              1.429022             0.071213   \n",
      "3             3              1.337411             0.062910   \n",
      "4             4              1.256561             0.056288   \n",
      "..          ...                   ...                  ...   \n",
      "495         495              0.035192             0.007550   \n",
      "496         496              0.035190             0.007552   \n",
      "497         497              0.035152             0.007584   \n",
      "498         498              0.035151             0.007583   \n",
      "499         499              0.035149             0.007583   \n",
      "\n",
      "     train-MultiClass-mean  train-MultiClass-std  \\\n",
      "0                 1.554294              0.028240   \n",
      "1                 1.525738              0.031253   \n",
      "2                 1.475868              0.040883   \n",
      "3                 1.419118              0.036678   \n",
      "4                 1.368458              0.029727   \n",
      "..                     ...                   ...   \n",
      "495               0.435178              0.002995   \n",
      "496               0.435116              0.002998   \n",
      "497               0.435004              0.002969   \n",
      "498               0.434957              0.002965   \n",
      "499               0.434909              0.002939   \n",
      "\n",
      "     test-Accuracy:use_weights=true-mean  test-Accuracy:use_weights=true-std  \\\n",
      "0                               0.801967                            0.291371   \n",
      "1                               0.807224                            0.282675   \n",
      "2                               0.859259                            0.208695   \n",
      "3                               0.968695                            0.033259   \n",
      "4                               0.987528                            0.003535   \n",
      "..                                   ...                                 ...   \n",
      "495                             0.991292                            0.003005   \n",
      "496                             0.991308                            0.002976   \n",
      "497                             0.991316                            0.003042   \n",
      "498                             0.991307                            0.003070   \n",
      "499                             0.991336                            0.003093   \n",
      "\n",
      "     train-Accuracy:use_weights=true-mean  \\\n",
      "0                                0.649919   \n",
      "1                                0.667918   \n",
      "2                                0.707788   \n",
      "3                                0.772824   \n",
      "4                                0.784292   \n",
      "..                                    ...   \n",
      "495                              0.813478   \n",
      "496                              0.813432   \n",
      "497                              0.813550   \n",
      "498                              0.813659   \n",
      "499                              0.813716   \n",
      "\n",
      "     train-Accuracy:use_weights=true-std  \\\n",
      "0                               0.157502   \n",
      "1                               0.160341   \n",
      "2                               0.118714   \n",
      "3                               0.013770   \n",
      "4                               0.006269   \n",
      "..                                   ...   \n",
      "495                             0.001126   \n",
      "496                             0.001215   \n",
      "497                             0.001116   \n",
      "498                             0.001127   \n",
      "499                             0.001146   \n",
      "\n",
      "     test-Accuracy:use_weights=false-mean  \\\n",
      "0                                0.813521   \n",
      "1                                0.818864   \n",
      "2                                0.870361   \n",
      "3                                0.970295   \n",
      "4                                0.987415   \n",
      "..                                    ...   \n",
      "495                              0.991280   \n",
      "496                              0.991297   \n",
      "497                              0.991313   \n",
      "498                              0.991297   \n",
      "499                              0.991330   \n",
      "\n",
      "     test-Accuracy:use_weights=false-std  \\\n",
      "0                               0.270593   \n",
      "1                               0.261312   \n",
      "2                               0.188987   \n",
      "3                               0.029898   \n",
      "4                               0.004073   \n",
      "..                                   ...   \n",
      "495                             0.002665   \n",
      "496                             0.002636   \n",
      "497                             0.002692   \n",
      "498                             0.002723   \n",
      "499                             0.002751   \n",
      "\n",
      "     train-Accuracy:use_weights=false-mean  \\\n",
      "0                                 0.674346   \n",
      "1                                 0.687091   \n",
      "2                                 0.724543   \n",
      "3                                 0.786797   \n",
      "4                                 0.798063   \n",
      "..                                     ...   \n",
      "495                               0.821359   \n",
      "496                               0.821325   \n",
      "497                               0.821443   \n",
      "498                               0.821552   \n",
      "499                               0.821603   \n",
      "\n",
      "     train-Accuracy:use_weights=false-std  \n",
      "0                                0.147744  \n",
      "1                                0.146966  \n",
      "2                                0.111162  \n",
      "3                                0.018087  \n",
      "4                                0.002408  \n",
      "..                                    ...  \n",
      "495                              0.000971  \n",
      "496                              0.001057  \n",
      "497                              0.000957  \n",
      "498                              0.000961  \n",
      "499                              0.000985  \n",
      "\n",
      "[500 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "separation = {'responsive': 4,\n",
    "              'very_responsive': 0,\n",
    "              'moderately_responsive': 3,\n",
    "              'very_moderately_responsive':2,\n",
    "              'unresponsive':1\n",
    "             }\n",
    "\n",
    "results = train_test_model(n_class=5, labels=separation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1364,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_5 = results\n",
    "results_5_train_accuracy_max = 0.813716\n",
    "results_5_test_accuracy_max = 0.991336"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as ../data/processed/procssed_class_3_latest.joblib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    display: inline-block;\n",
       "    padding: 5px 10px 0 0;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart_active {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
       "    white-space: nowrap;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "#catboost-control-test {\n",
       "    margin-left: 11px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_label {\n",
       "    padding: 0 0 0 8px;\n",
       "    width: 200px;\n",
       "    text-overflow: ellipsis;\n",
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       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
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       "\n",
       ".catboost-panel__serie__learn_hint {\n",
       "    top: 56px;\n",
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       "\n",
       ".catboost-panel__serie__test_hint {\n",
       "    top: 82px;\n",
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       "\n",
       ".catboost-panel__serie_bottom {\n",
       "    padding-bottom: 6px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_time {\n",
       "    position: absolute;\n",
       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
       "    top: -3px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic {\n",
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       "\n",
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       "\n",
       ".catboost-panel__serie-value {\n",
       "    display: inline-block;\n",
       "    min-width: 30px;\n",
       "    margin-right: 2px;\n",
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       "\n",
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       "\n",
       ".catboost-panel__serie_names {\n",
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       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
       ".catboost-panel__serie_best_test_value {\n",
       "    width: 85px;\n",
       "    position: relative;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c95f44a5694845698438c71d7c4d3f9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.06899800151586533'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.9993279569892473'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Weights: [2.9532769639950778, 2.739780511158577, 3.373815246474083]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5058    1    3]\n",
      " [   1 5390    2]\n",
      " [   2    1 4422]]\n",
      "Starting the trainig with 3-Fold validation using Catboost CV:\n"
     ]
    },
    {
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       "    height: 100%;\n",
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       "    box-sizing: border-box;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de690843caad408dae4d95367e4c57d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     iterations  test-MultiClass-mean  test-MultiClass-std  \\\n",
      "0             0              1.057096             0.018236   \n",
      "1             1              1.002712             0.016703   \n",
      "2             2              0.952778             0.015361   \n",
      "3             3              0.906764             0.014279   \n",
      "4             4              0.864170             0.013174   \n",
      "..          ...                   ...                  ...   \n",
      "495         495              0.022375             0.004774   \n",
      "496         496              0.022349             0.004796   \n",
      "497         497              0.022342             0.004799   \n",
      "498         498              0.022344             0.004804   \n",
      "499         499              0.022343             0.004804   \n",
      "\n",
      "     train-MultiClass-mean  train-MultiClass-std  \\\n",
      "0                 1.065032              0.010427   \n",
      "1                 1.025768              0.008670   \n",
      "2                 0.989836              0.007270   \n",
      "3                 0.956837              0.005912   \n",
      "4                 0.926004              0.005708   \n",
      "..                     ...                   ...   \n",
      "495               0.254407              0.002064   \n",
      "496               0.254346              0.002004   \n",
      "497               0.254272              0.002032   \n",
      "498               0.254243              0.002037   \n",
      "499               0.254206              0.002056   \n",
      "\n",
      "     test-Accuracy:use_weights=true-mean  test-Accuracy:use_weights=true-std  \\\n",
      "0                               0.871268                            0.166657   \n",
      "1                               0.991354                            0.003717   \n",
      "2                               0.991795                            0.003228   \n",
      "3                               0.992083                            0.003586   \n",
      "4                               0.991694                            0.003393   \n",
      "..                                   ...                                 ...   \n",
      "495                             0.992923                            0.002546   \n",
      "496                             0.992908                            0.002573   \n",
      "497                             0.992942                            0.002514   \n",
      "498                             0.992957                            0.002488   \n",
      "499                             0.992957                            0.002488   \n",
      "\n",
      "     train-Accuracy:use_weights=true-mean  \\\n",
      "0                                0.788618   \n",
      "1                                0.862040   \n",
      "2                                0.862003   \n",
      "3                                0.859497   \n",
      "4                                0.862061   \n",
      "..                                    ...   \n",
      "495                              0.884967   \n",
      "496                              0.885044   \n",
      "497                              0.885096   \n",
      "498                              0.885126   \n",
      "499                              0.885064   \n",
      "\n",
      "     train-Accuracy:use_weights=true-std  \\\n",
      "0                               0.095082   \n",
      "1                               0.012404   \n",
      "2                               0.010898   \n",
      "3                               0.012736   \n",
      "4                               0.011914   \n",
      "..                                   ...   \n",
      "495                             0.001507   \n",
      "496                             0.001498   \n",
      "497                             0.001531   \n",
      "498                             0.001584   \n",
      "499                             0.001531   \n",
      "\n",
      "     test-Accuracy:use_weights=false-mean  \\\n",
      "0                                0.873537   \n",
      "1                                0.991750   \n",
      "2                                0.992204   \n",
      "3                                0.992540   \n",
      "4                                0.992153   \n",
      "..                                    ...   \n",
      "495                              0.993178   \n",
      "496                              0.993162   \n",
      "497                              0.993195   \n",
      "498                              0.993212   \n",
      "499                              0.993212   \n",
      "\n",
      "     test-Accuracy:use_weights=false-std  \\\n",
      "0                               0.160833   \n",
      "1                               0.003246   \n",
      "2                               0.002772   \n",
      "3                               0.003157   \n",
      "4                               0.002941   \n",
      "..                                   ...   \n",
      "495                             0.002326   \n",
      "496                             0.002355   \n",
      "497                             0.002297   \n",
      "498                             0.002268   \n",
      "499                             0.002268   \n",
      "\n",
      "     train-Accuracy:use_weights=false-mean  \\\n",
      "0                                 0.797052   \n",
      "1                                 0.869094   \n",
      "2                                 0.869144   \n",
      "3                                 0.868220   \n",
      "4                                 0.870648   \n",
      "..                                     ...   \n",
      "495                               0.888501   \n",
      "496                               0.888576   \n",
      "497                               0.888627   \n",
      "498                               0.888652   \n",
      "499                               0.888593   \n",
      "\n",
      "     train-Accuracy:use_weights=false-std  \n",
      "0                                0.093951  \n",
      "1                                0.007637  \n",
      "2                                0.006683  \n",
      "3                                0.008888  \n",
      "4                                0.008215  \n",
      "..                                    ...  \n",
      "495                              0.001469  \n",
      "496                              0.001459  \n",
      "497                              0.001490  \n",
      "498                              0.001540  \n",
      "499                              0.001487  \n",
      "\n",
      "[500 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "separation = {'responsive': 2,\n",
    "              'very_responsive': 0,\n",
    "              'unresponsive':1\n",
    "             }\n",
    "\n",
    "results = train_test_model(n_class=3, labels=separation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1367,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_3 = results\n",
    "results_3_train_accuracy_max = 0.885064 \n",
    "results_3_test_accuracy_max = 0.992957 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved as ../data/processed/procssed_class_2_latest.joblib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <style>\n",
       "                .highcharts-tooltip {\n",
       "    display: none !important;\n",
       "}\n",
       ".highcharts-halo {\n",
       "    display: none !important;\n",
       "}\n",
       "\n",
       ".catboost {\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel {\n",
       "    position: absolute;\n",
       "    height: 100%;\n",
       "    width: 280px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls_label {\n",
       "    padding: 5px 0 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 80px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls_label_time {\n",
       "    width: inherit;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2 {\n",
       "    margin-top: 10px;\n",
       "}\n",
       "\n",
       ".catboost-panel__controls2_label {\n",
       "    padding: 5px 11px 0 8px;\n",
       "    cursor: pointer;\n",
       "    width: 90px;\n",
       "    box-sizing: content-box;\n",
       "}\n",
       ".catboost-panel__controls2_label-long {\n",
       "    width: 170px;\n",
       "}\n",
       "\n",
       ".catboost-panel__series {\n",
       "    height: 340px;\n",
       "    overflow-y: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph {\n",
       "    margin-left: 290px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tabs {\n",
       "    padding: 0 0 0 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab {\n",
       "    display: inline-block;\n",
       "    padding: 5px 10px 0 0;\n",
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       "\n",
       ".catboost-graph__tab {\n",
       "    color: #999;\n",
       "    cursor: pointer;\n",
       "    transition: color 0.1s linear;\n",
       "}\n",
       "\n",
       ".catboost-graph__tab:hover {\n",
       "    color: #333;\n",
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       "\n",
       ".catboost-graph__tab_active {\n",
       "    color: #000;\n",
       "    cursor: auto;\n",
       "}\n",
       "\n",
       ".catboost-graph__charts {\n",
       "    padding-top: 20px;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       ".catboost-graph__chart_active {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie {\n",
       "    padding-bottom: 5px;\n",
       "    border-bottom: 1px solid rgba(0, 0, 0, 0.3);\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_bottom,\n",
       ".catboost-panel__serie_middle,\n",
       ".catboost-panel__serie_top {\n",
       "    white-space: nowrap;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "#catboost-control-test {\n",
       "    margin-left: 11px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_label {\n",
       "    padding: 0 0 0 8px;\n",
       "    width: 200px;\n",
       "    text-overflow: ellipsis;\n",
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       "    margin-bottom: 0;\n",
       "    overflow: hidden;\n",
       "    position: relative;\n",
       "    top: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_hint {\n",
       "    position: absolute;\n",
       "    font-size: 9px;\n",
       "    left: 0;\n",
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       "\n",
       ".catboost-panel__serie__learn_hint {\n",
       "    top: 56px;\n",
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       "\n",
       ".catboost-panel__serie__test_hint {\n",
       "    top: 82px;\n",
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       "    padding-bottom: 6px;\n",
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       "\n",
       ".catboost-panel__serie_time {\n",
       "    position: absolute;\n",
       "    top: 5px;\n",
       "    right: 2px;\n",
       "    height: 20px;\n",
       "    padding: 0 0 0 20px;\n",
       "    margin-bottom: 3px;\n",
       "    overflow: hidden;\n",
       "\n",
       "    text-overflow: ellipsis;\n",
       "    box-sizing: border-box;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic,\n",
       ".catboost-panel__serie_test_pic {\n",
       "    width: 13px;\n",
       "    height: 1px;\n",
       "    border-top-width: 1px;\n",
       "    position: relative;\n",
       "    top: -3px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_pic {\n",
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       ".catboost-panel__serie-value {\n",
       "    display: inline-block;\n",
       "    min-width: 30px;\n",
       "    margin-right: 2px;\n",
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       ".catboost-panel__serie_names {\n",
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       "\n",
       ".catboost-panel__serie_scroll {\n",
       "    width: 240px;\n",
       "    overflow-x: auto;\n",
       "    margin-left: 20px;\n",
       "}\n",
       "\n",
       ".catboost-panel__serie_learn_name,\n",
       ".catboost-panel__serie_test_name,\n",
       ".catboost-panel__serie_learn_value,\n",
       ".catboost-panel__serie_test_value,\n",
       ".catboost-panel__serie_best_learn_value,\n",
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       "    width: 85px;\n",
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7d2b2319bc54597a1ff098023ad0cff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Learning Rate set to: 0.06899800151586533'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Accuracy Score: 0.9996639784946236'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Weights: [1.9532148389298747, 2.0490814443495746]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7682    3]\n",
      " [   2 7193]]\n",
      "Starting the trainig with 3-Fold validation using Catboost CV:\n"
     ]
    },
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o=t(\"d3\"),i=t(\"../../lib\"),l=t(\"../dragelement\"),s=t(\"./helpers\"),c=t(\"./layout_attributes\");e.exports={moduleType:\"component\",name:\"fx\",constants:t(\"./constants\"),schema:{layout:c},attributes:t(\"./attributes\"),layoutAttributes:c,supplyLayoutGlobalDefaults:t(\"./layout_global_defaults\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\"),getDistanceFunction:s.getDistanceFunction,getClosest:s.getClosest,inbox:s.inbox,castHoverOption:a,hover:t(\"./hover\").hover,unhover:l.unhover,loneHover:t(\"./hover\").loneHover,loneUnhover:n,click:t(\"./click\")}},{\"../../lib\":136,\"../dragelement\":46,\"./attributes\":58,\"./calc\":59,\"./click\":60,\"./constants\":61,\"./defaults\":62,\"./helpers\":63,\"./hover\":64,\"./layout_attributes\":67,\"./layout_defaults\":68,\"./layout_global_defaults\":69,d3:7}],67:[function(t,e,r){\"use strict\";var 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I=this.container.selectAll(\"rect.scrollbar-vertical\").data(O?[0]:[]);I.exit().on(\".drag\",null).remove(),I.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(o.fill,n.barColor),O?(this.vbar=I.attr({rx:n.barRadius,ry:n.barRadius,x:E,y:N,width:D,height:P}),this._vbarYMin=N+P/2,this._vbarTranslateMax=M-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll(\"#\"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),A||O?(this._clipRect=H.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var 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r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=l.constrain(t||0,0,r),e=l.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(i.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var a=t/r;this.hbar.call(i.setTranslate,t+a*this._hbarTranslateMax,e)}if(this.vbar){var o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{\"../../lib\":136,\"../color\":25,\"../drawing\":49,d3:7}],121:[function(t,e,r){\"use strict\";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){\"use strict\";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){\"use 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b,_=[f.margin.left+x,f.margin.top+x];if(p){b=[0,n.max(i.util.sumArrays(i.util.arrayLast(h).r[0],i.util.arrayLast(m)))]}else b=n.extent(i.util.flattenArray(h.map(function(t,e){return t.r})));f.radialAxis.domain!=i.DATAEXTENT&&(b[0]=0),a=n.scale.linear().domain(f.radialAxis.domain!=i.DATAEXTENT&&f.radialAxis.domain?f.radialAxis.domain:b).range([0,x]),u.layout.radialAxis.domain=a.domain();var w,k=i.util.flattenArray(h.map(function(t,e){return t.t})),M=\"string\"==typeof k[0];M&&(k=i.util.deduplicate(k),w=k.slice(),k=n.range(k.length),h=h.map(function(t,e){var r=t;return t.t=[k],p&&(r.yStack=t.yStack),r}));var A=h.filter(function(t,e){return\"LinePlot\"===t.geometry||\"DotPlot\"===t.geometry}).length===h.length,T=null===f.needsEndSpacing?M||!A:f.needsEndSpacing,L=f.angularAxis.domain&&f.angularAxis.domain!=i.DATAEXTENT&&!M&&f.angularAxis.domain[0]>=0,C=L?f.angularAxis.domain:n.extent(k),S=Math.abs(k[1]-k[0]);A&&!M&&(S=0);var z=C.slice();T&&M&&(z[1]+=S);var O=f.angularAxis.ticksCount||4;O>8&&(O=O/(O/8)+O%8),f.angularAxis.ticksStep&&(O=(z[1]-z[0])/O);var D=f.angularAxis.ticksStep||(z[1]-z[0])/(O*(f.minorTicks+1));w&&(D=Math.max(Math.round(D),1)),z[2]||(z[2]=D);var P=n.range.apply(this,z);if(P=P.map(function(t,e){return parseFloat(t.toPrecision(12))}),l=n.scale.linear().domain(z.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=l.domain(),u.layout.angularAxis.endPadding=T?S:0,void 0===(e=n.select(this).select(\"svg.chart-root\"))||e.empty()){var E=(new DOMParser).parseFromString(\"<svg xmlns='http://www.w3.org/2000/svg' class='chart-root'>' + '<g class='outer-group'>' + '<g class='chart-group'>' + '<circle class='background-circle'></circle>' + '<g class='geometry-group'></g>' + '<g class='radial axis-group'>' + '<circle class='outside-circle'></circle>' + '</g>' + '<g class='angular axis-group'></g>' + '<g class='guides-group'><line></line><circle r='0'></circle></g>' + '</g>' + '<g 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r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=void 0===t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r});i.Legend().config({data:h.map(function(t,e){return t.name||\"Element\"+e}),legendConfig:o({},i.Legend.defaultConfig().legendConfig,{container:I,elements:B,reverseOrder:f.legend.reverseOrder})})();var q=I.node().getBBox();x=Math.min(f.width-q.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],a.range([0,x]),u.layout.radialAxis.domain=a.domain(),I.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else I=e.select(\".legend-group\").style({display:\"none\"});e.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var H=[(f.width-(f.margin.left+f.margin.right+2*x+(q?q.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(H[0]=Math.max(0,H[0]),H[1]=Math.max(0,H[1]),e.select(\".outer-group\").attr(\"transform\",\"translate(\"+H+\")\"),f.title){var V=e.select(\"g.title-group text\").style(j).text(f.title),U=V.node().getBBox();V.attr({x:_[0]-U.width/2,y:_[1]-x-20})}var X=e.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=X.selectAll(\"circle.grid-circle\").data(a.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",a),G.exit().remove()}X.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=e.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});if(f.radialAxis.visible){var Z=n.svg.axis().scale(a).ticks(5).tickSize(5);X.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),X.selectAll(\".domain\").style(F),X.selectAll(\"g>text\").text(function(t,e){return this.textContent+f.radialAxis.ticksSuffix}).style(j).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,j[\"font-size\"]]+\")\":\"translate(\"+[0,j[\"font-size\"]]+\")\"}}),X.selectAll(\"g>line\").style({stroke:\"black\"})}var W=e.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(P),$=W.enter().append(\"g\").classed(\"angular-tick\",!0);W.attr({transform:function(t,e){return\"rotate(\"+s(t,e)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),W.exit().remove(),$.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",function(t,e){return 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e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)});var J=n.max(R.selectAll(\".angular-tick text\")[0].map(function(t,e){return t.getCTM().e+t.getBBox().width}));I.attr({transform:\"translate(\"+[x+J,f.margin.top]+\")\"});var K=e.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=e.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(h);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),h[0]||K){var et=[];h.forEach(function(t,e){var r={};r.radialScale=a,r.angularScale=l,r.container=tt.filter(function(t,r){return r==e}),r.geometry=t.geometry,r.orientation=f.orientation,r.direction=f.direction,r.index=e,et.push({data:t,geometryConfig:r})});var rt=n.nest().key(function(t,e){return void 0!==t.data.groupId||\"unstacked\"}).entries(et),nt=[];rt.forEach(function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map(function(t,e){return[t]})):nt.push(t.values)}),nt.forEach(function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map(function(t,e){return o(i[r].defaultConfig(),t)});i[r]().config(n)()})}var at,ot,it=e.select(\".guides-group\"),lt=e.select(\".tooltips-group\"),st=i.tooltipPanel().config({container:lt,fontSize:8})(),ct=i.tooltipPanel().config({container:lt,fontSize:8})(),ut=i.tooltipPanel().config({container:lt,hasTick:!0})();if(!M){var ft=it.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",function(t,e){var r=i.util.getMousePos(Y).angle;ft.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;at=l.invert(n);var a=i.util.convertToCartesian(x+12,r+180);st.text(i.util.round(at)).move([a[0]+_[0],a[1]+_[1]])}).on(\"mouseout.angular-guide\",function(t,e){it.select(\"line\").style({opacity:0})})}var dt=it.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",function(t,e){var r=i.util.getMousePos(Y).radius;dt.attr({r:r}).style({opacity:.5}),ot=a.invert(i.util.getMousePos(Y).radius);var n=i.util.convertToCartesian(r,f.radialAxis.orientation);ct.text(i.util.round(ot)).move([n[0]+_[0],n[1]+_[1]])}).on(\"mouseout.radial-guide\",function(t,e){dt.style({opacity:0}),ut.hide(),st.hide(),ct.hide()}),e.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",function(t,r){var a=n.select(this),o=a.style(\"fill\"),l=\"black\",s=a.style(\"opacity\")||1;if(a.attr({\"data-opacity\":s}),\"none\"!=o){a.attr({\"data-fill\":o}),l=n.hsl(o).darker().toString(),a.style({fill:l,opacity:1});var c={t:i.util.round(t[0]),r:i.util.round(t[1])};M&&(c.t=w[t[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,f=this.getBoundingClientRect(),d=e.node().getBoundingClientRect(),h=[f.left+f.width/2-H[0]-d.left,f.top+f.height/2-H[1]-d.top];ut.config({color:l}).text(u),ut.move(h)}else 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l},d.svg=function(){return e},n.rebind(d,f,\"on\"),d},i.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, 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n=e*Math.PI/180;return[e,t(n)]})},i.util.dataFromEquation=function(t,e,r){var a=e||6,o=[],i=[];n.range(0,360+a,a).forEach(function(e,r){var n=e*Math.PI/180,a=t(n);o.push(e),i.push(a)});var l={t:o,r:i};return r&&(l.name=r),l},i.util.ensureArray=function(t,e){if(void 0===t)return null;var r=[].concat(t);return n.range(e).map(function(t,e){return r[e]||r[0]})},i.util.fillArrays=function(t,e,r){return e.forEach(function(e,n){t[e]=i.util.ensureArray(t[e],r)}),t},i.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},i.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},i.util.sumArrays=function(t,e){return n.zip(t,e).map(function(t,e){return n.sum(t)})},i.util.arrayLast=function(t){return t[t.length-1]},i.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return r===-2},i.util.flattenArray=function(t){for(var e=[];!i.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},i.util.deduplicate=function(t){return t.filter(function(t,e,r){return r.indexOf(t)==e})},i.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},i.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},i.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],a=e[1],o={};return o.x=r,o.y=a,o.pos=e,o.angle=180*(Math.atan2(a,r)+Math.PI)/Math.PI,o.radius=Math.sqrt(r*r+a*a),o},i.util.duplicatesCount=function(t){for(var e,r={},n={},a=0,o=t.length;a<o;a++)e=t[a],e in r?(r[e]++,n[e]=r[e]):r[e]=1;return n},i.util.duplicates=function(t){return Object.keys(i.util.duplicatesCount(t))},i.util.translator=function(t,e,r,n){if(n){var a=r.slice();r=e,e=a}var o=e.reduce(function(t,e){if(void 0!==t)return t[e]},t);void 0!==o&&(e.reduce(function(t,r,n){if(void 0!==t)return n===e.length-1&&delete t[r],t[r]},t),r.reduce(function(t,e,n){return void 0===t[e]&&(t[e]={}),n===r.length-1&&(t[e]=o),t[e]},t))},i.PolyChart=function(){function t(){var t=e[0].geometryConfig,r=t.container;\"string\"==typeof r&&(r=n.select(r)),r.datum(e).each(function(e,r){function o(e,r){return{r:t.radialScale(e[1]),t:(t.angularScale(e[0])+t.orientation)*Math.PI/180}}function i(t){return{x:t.r*Math.cos(t.t),y:t.r*Math.sin(t.t)}}var l=!!e[0].data.yStack,s=e.map(function(t,e){return l?n.zip(t.data.t[0],t.data.r[0],t.data.yStack[0]):n.zip(t.data.t[0],t.data.r[0])}),c=t.angularScale,u=t.radialScale.domain()[0],f={};f.bar=function(r,a,o){var i=e[o].data,l=t.radialScale(r[1])-t.radialScale(0),s=t.radialScale(r[2]||0),u=i.barWidth;n.select(this).attr({class:\"mark bar\",d:\"M\"+[[l+s,-u/2],[l+s,u/2],[s,u/2],[s,-u/2]].join(\"L\")+\"Z\",transform:function(e,r){return\"rotate(\"+(t.orientation+c(e[0]))+\")\"}})},f.dot=function(t,r,a){var l=t[2]?[t[0],t[1]+t[2]]:t,s=n.svg.symbol().size(e[a].data.dotSize).type(e[a].data.dotType)(t,r);n.select(this).attr({class:\"mark 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.supplyLayoutDefaults=t(\"./layout_defaults\"),n.layoutAttributes=t(\"./layout_attributes\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.styleOne=t(\"./style_one\"),n.moduleType=\"trace\",n.name=\"pie\",n.basePlotModule=t(\"./base_plot\"),n.categories=[\"pie\",\"showLegend\"],n.meta={},e.exports=n},{\"./attributes\":228,\"./base_plot\":229,\"./calc\":230,\"./defaults\":231,\"./layout_attributes\":234,\"./layout_defaults\":235,\"./plot\":236,\"./style\":237,\"./style_one\":238}],234:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\"}}},{}],235:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),a=t(\"./layout_attributes\");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}(\"hiddenlabels\")}},{\"../../lib\":136,\"./layout_attributes\":234}],236:[function(t,e,r){\"use strict\";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var n,a,i,f,d,p,g=r.labelExtraY+(o?r.yLabelMax:r.yLabelMin),v=o?t.yLabelMin:t.yLabelMax,m=o?t.yLabelMax:t.yLabelMin,y=t.cyFinal+s(t.px0[1],t.px1[1]),x=g-v;if(x*u>0&&(t.labelExtraY=x),Array.isArray(e.pull))for(a=0;a<h.length;a++)(i=h[a])===t||(e.pull[t.i]||0)>=e.pull[i.i]||((t.pxmid[1]-i.pxmid[1])*u>0?(f=i.cyFinal+s(i.px0[1],i.px1[1]),(x=f-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x)):(m+t.labelExtraY-y)*u>0&&(n=3*c*Math.abs(a-h.indexOf(t)),d=i.cxFinal+l(i.px0[0],i.px1[0]),(p=d+n-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*c>0&&(t.labelExtraX+=p)))}(g[p],m)}}}function l(t,e){var r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2,\n",
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n=Math.sin(t*Math.PI/180);return Math.max(.01,r*n*Math.abs(e)+2*Math.sqrt(1-n*n*e*e))}var u=t(\"d3\"),f=t(\"../../components/fx\"),d=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../lib/svg_text_utils\"),g=t(\"./helpers\");e.exports=function(t,e){var r=t._fullLayout;l(e,r._size);var c=r._pielayer.selectAll(\"g.trace\").data(e);c.enter().append(\"g\").attr({\"stroke-linejoin\":\"round\",class:\"trace\"}),c.exit().remove(),c.order(),c.each(function(e){var l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll(\"g.part\").data(v.tilt?[\"top\",\"sides\"]:[\"top\"]);w.enter().append(\"g\").attr(\"class\",function(t){return t+\" part\"}),w.exit().remove(),w.order(),s(e),l.selectAll(\".top\").each(function(){var l=u.select(this).selectAll(\"g.slice\").data(e);l.enter().append(\"g\").classed(\"slice\",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if(\"all\"===l&&(l=\"label+text+value+percent+name\"),t._dragging||o.hovermode===!1||\"none\"===l||\"skip\"===l||!l)return void f.hover(t,n,\"pie\");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf(\"label\")!==-1&&m.push(e.label),l.indexOf(\"text\")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf(\"value\")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf(\"percent\")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join(\"<br>\"),name:l.indexOf(\"name\")!==-1?i.name:void 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w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll(\"path.surface\").data([e]),T=!1;if(A.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),M.select(\"path.textline\").remove(),M.on(\"mouseover\",i).on(\"mouseout\",l).on(\"click\",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S=\"M\"+(w+e.px0[0])+\",\"+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+\"Z\";C?A.attr(\"d\",\"M\"+(w+C*e.px0[0])+\",\"+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+\"Z\"+S):A.attr(\"d\",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr(\"d\",\"M\"+(w+C*e.px1[0])+\",\"+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+\"l\"+O*e.px0[0]+\",\"+O*e.px0[1]+z+\"Z\")}else A.attr(\"d\",\"M\"+w+\",\"+k+\"l\"+e.px0[0]+\",\"+e.px0[1]+z+\"Z\")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll(\"g.slicetext\").data(e.text&&\"none\"!==D?[0]:[]);P.enter().append(\"g\").classed(\"slicetext\",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll(\"text\").data([0]);t.enter().append(\"text\").attr(\"data-notex\",1),t.exit().remove(),t.text(e.text).attr({class:\"slicetext\",transform:\"\",\"data-bb\":\"\",\"text-anchor\":\"middle\",x:0,y:0}).call(h.font,\"outside\"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll(\"tspan.line\").attr({x:0,y:0});var r,a=h.bBox(t.node());\"outside\"===D?r=o(a,e):(r=n(a,e,c),\"auto\"===D&&r.scale<1&&(t.call(h.font,v.outsidetextfont),v.outsidetextfont.family===v.insidetextfont.family&&v.outsidetextfont.size===v.insidetextfont.size||(t.attr({\"data-bb\":\"\"}),a=h.bBox(t.node())),r=o(a,e)));var i=w+e.pxmid[0]*r.rCenter+(r.x||0),l=k+e.pxmid[1]*r.rCenter+(r.y||0);r.outside&&(e.yLabelMin=l-a.height/2,e.yLabelMid=l,e.yLabelMax=l+a.height/2,e.labelExtraX=0,e.labelExtraY=0,m=!0),t.attr(\"transform\",\"translate(\"+i+\",\"+l+\")\"+(r.scale<1?\"scale(\"+r.scale+\")\":\"\")+(r.rotate?\"rotate(\"+r.rotate+\")\":\"\")+\"translate(\"+-(a.left+a.right)/2+\",\"+-(a.top+a.bottom)/2+\")\")})}),m&&i(s,v),l.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=u.select(this),r=e.select(\"g.slicetext text\");r.attr(\"transform\",\"translate(\"+t.labelExtraX+\",\"+t.labelExtraY+\")\"+r.attr(\"transform\"));var n=t.cxFinal+t.pxmid[0],a=t.cyFinal+t.pxmid[1],o=\"M\"+n+\",\"+a,i=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var l=t.labelExtraX*t.pxmid[1]/t.pxmid[0],s=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(l)>Math.abs(s)?o+=\"l\"+s*t.pxmid[0]/t.pxmid[1]+\",\"+s+\"H\"+(n+t.labelExtraX+i):o+=\"l\"+t.labelExtraX+\",\"+l+\"v\"+(s-l)+\"h\"+i}else o+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+i;e.append(\"path\").classed(\"textline\",!0).call(d.stroke,v.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,v.outsidetextfont.size/8),d:o,fill:\"none\"})}})})}),setTimeout(function(){c.selectAll(\"tspan\").each(function(){var t=u.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))})},0)}},{\"../../components/color\":25,\"../../components/drawing\":49,\"../../components/fx\":66,\"../../lib/svg_text_utils\":153,\"./helpers\":232,d3:7}],237:[function(t,e,r){\"use strict\";var n=t(\"d3\"),a=t(\"./style_one\");e.exports=function(t){t._fullLayout._pielayer.selectAll(\".trace\").each(function(t){var e=t[0],r=e.trace,o=n.select(this);o.style({opacity:r.opacity}),o.selectAll(\".top path.surface\").each(function(t){n.select(this).call(a,t,r)})})}},{\"./style_one\":238,d3:7}],238:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\");e.exports=function(t,e,r){var a=r.marker.line.color;Array.isArray(a)&&(a=a[e.i]||n.defaultLine);var o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({\"stroke-width\":o}).call(n.fill,e.color).call(n.stroke,a)}},{\"../../components/color\":25}],239:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArray(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var a=e.marker;if(a){n.mergeArray(a.size,t,\"ms\"),n.mergeArray(a.opacity,t,\"mo\"),n.mergeArray(a.symbol,t,\"mx\"),n.mergeArray(a.color,t,\"mc\");var o=a.line;a.line&&(n.mergeArray(o.color,t,\"mlc\"),n.mergeArray(o.width,t,\"mlw\"));var i=a.gradient;i&&\"none\"!==i.type&&(n.mergeArray(i.type,t,\"mgt\"),n.mergeArray(i.color,t,\"mgc\"))}}},{\"../../lib\":136}],240:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../components/errorbars/attributes\"),o=t(\"../../components/colorbar/attributes\"),i=t(\"../../components/drawing/attributes\").dash,l=t(\"../../components/drawing\"),s=(t(\"./constants\"),t(\"../../lib/extend\").extendFlat);e.exports={x:{valType:\"data_array\"},x0:{valType:\"any\",dflt:0},dx:{valType:\"number\",dflt:1},y:{valType:\"data_array\"},y0:{valType:\"any\",dflt:0},customdata:{valType:\"data_array\"},dy:{valType:\"number\",dflt:1},ids:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\",\"text\"],extras:[\"none\"]},hoveron:{valType:\"flaglist\",flags:[\"points\",\"fills\"]},line:{color:{valType:\"color\"},width:{valType:\"number\",min:0,dflt:2},shape:{valType:\"enumerated\",values:[\"linear\",\"spline\",\"hv\",\"vh\",\"hvh\",\"vhv\"],dflt:\"linear\"},smoothing:{valType:\"number\",min:0,max:1.3,dflt:1},dash:i,simplify:{valType:\"boolean\",dflt:!0}},connectgaps:{valType:\"boolean\",dflt:!1},fill:{valType:\"enumerated\",values:[\"none\",\"tozeroy\",\"tozerox\",\"tonexty\",\"tonextx\",\"toself\",\"tonext\"],dflt:\"none\"},fillcolor:{valType:\"color\"},marker:s({},{symbol:{valType:\"enumerated\",values:l.symbolList,dflt:\"circle\",arrayOk:!0},opacity:{valType:\"number\",min:0,max:1,arrayOk:!0},size:{valType:\"number\",min:0,dflt:6,arrayOk:!0},maxdisplayed:{valType:\"number\",min:0,dflt:0},sizeref:{valType:\"number\",dflt:1},sizemin:{valType:\"number\",min:0,dflt:0},sizemode:{valType:\"enumerated\",values:[\"diameter\",\"area\"],dflt:\"diameter\"},showscale:{valType:\"boolean\",dflt:!1},colorbar:o,line:s({},{width:{valType:\"number\",min:0,arrayOk:!0}},n(\"marker.line\")),gradient:{type:{valType:\"enumerated\",values:[\"radial\",\"horizontal\",\"vertical\",\"none\"],arrayOk:!0,dflt:\"none\"},color:{valType:\"color\",arrayOk:!0}}},n(\"marker\")),textposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle left\",\"middle center\",\"middle right\",\"bottom left\",\"bottom center\",\"bottom right\"],dflt:\"middle center\",arrayOk:!0},textfont:{family:{valType:\"string\",noBlank:!0,strict:!0,arrayOk:!0},size:{valType:\"number\",min:1,arrayOk:!0},color:{valType:\"color\",arrayOk:!0}},r:{valType:\"data_array\"},t:{valType:\"data_array\"},error_y:a,error_x:a}},{\"../../components/colorbar/attributes\":26,\"../../components/colorscale/color_attributes\":32,\"../../components/drawing\":49,\"../../components/drawing/attributes\":48,\"../../components/errorbars/attributes\":51,\"../../lib/extend\":132,\"./constants\":245}],241:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),a=t(\"../../plots/cartesian/axes\"),o=t(\"../../constants/numerical\").BADNUM,i=t(\"./subtypes\"),l=t(\"./colorscale_calc\"),s=t(\"./arrays_to_calcdata\");e.exports=function(t,e){var r,c,u,f=a.getFromId(t,e.xaxis||\"x\"),d=a.getFromId(t,e.yaxis||\"y\"),h=f.makeCalcdata(e,\"x\"),p=d.makeCalcdata(e,\"y\"),g=Math.min(h.length,p.length);f._minDtick=0,d._minDtick=0,h.length>g&&h.splice(g,h.length-g),p.length>g&&p.splice(g,p.length-g);var v={padded:!0},m={padded:!0};if(i.hasMarkers(e)){if(r=e.marker,c=r.size,Array.isArray(c)){var y={type:\"linear\"};a.setConvert(y),c=y.makeCalcdata(e.marker,\"size\"),c.length>g&&c.splice(g,c.length-g)}var x,b=1.6*(e.marker.sizeref||1);x=\"area\"===e.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/b),3)}:function(t){return 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o,i=a(\"x\"),l=a(\"y\");if(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a(\"y0\"),a(\"dy\"));else{if(!l)return 0;o=e.y.length,a(\"x0\"),a(\"dx\")}return o}},{\"../../registry\":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/\n",
       "/* eslint-disable */\n",
       "function throttle(fn, timeout, invokeAsap, ctx) {\n",
       "    var typeofInvokeAsap = typeof invokeAsap;\n",
       "    if(typeofInvokeAsap === 'undefined') {\n",
       "        invokeAsap = true;\n",
       "    } else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {\n",
       "        ctx = invokeAsap;\n",
       "        invokeAsap = true;\n",
       "    }\n",
       "\n",
       "    var timer, args, needInvoke,\n",
       "        wrapper = function() {\n",
       "            if(needInvoke) {\n",
       "                fn.apply(ctx, args);\n",
       "                needInvoke = false;\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "            } else {\n",
       "                timer = null;\n",
       "            }\n",
       "        };\n",
       "\n",
       "    return function() {\n",
       "        args = arguments;\n",
       "        ctx || (ctx = this);\n",
       "        needInvoke = true;\n",
       "\n",
       "        if(!timer) {\n",
       "            invokeAsap?\n",
       "                wrapper() :\n",
       "                timer = setTimeout(wrapper, timeout);\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "/* eslint-enable */\n",
       "function CatboostIpython() {\n",
       "\n",
       "}\n",
       "\n",
       "CatboostIpython.prototype.init = function() {\n",
       "    this.charts = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                ...\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "    this.traces = {};\n",
       "    /*\n",
       "        {\n",
       "            \"rmse_1\": {\n",
       "                name: \"rmse\",\n",
       "                id: \"rmse_1\",\n",
       "                parent: \"div\",\n",
       "                traces: [\n",
       "                    {\n",
       "                        name: \"current;learn;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;learn;1;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;smoothed;;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;best_point;\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    },\n",
       "                    {\n",
       "                        name: \"current;test;0;;;best_value\",\n",
       "                        x: [],\n",
       "                        y: []\n",
       "                    }\n",
       "                ]\n",
       "            }\n",
       "        }\n",
       "    */\n",
       "\n",
       "    this.hovertextParameters = [];\n",
       "    this.chartsToRedraw = {};\n",
       "    this.lastIndexes = {};\n",
       "    this.smoothness = -1;\n",
       "    this.layoutDisabled = {\n",
       "        series: {},\n",
       "        traces: {}\n",
       "    };\n",
       "    this.clickMode = false;\n",
       "    this.logarithmMode = 'linear';\n",
       "    this.lastSmooth = 0;\n",
       "    this.layout = null;\n",
       "    this.activeTab = '';\n",
       "    this.meta = {};\n",
       "    this.timeLeft = {};\n",
       "\n",
       "    this.hasCVMode = false;\n",
       "    this.stddevEnabled = false;\n",
       "\n",
       "    this.colors = [\n",
       "        '#68E256',\n",
       "        '#56AEE2',\n",
       "        '#CF56E2',\n",
       "        '#E28956',\n",
       "        '#56E289',\n",
       "        '#5668E2',\n",
       "        '#E256AE',\n",
       "        '#E2CF56',\n",
       "        '#56E2CF',\n",
       "        '#8A56E2',\n",
       "        '#E25668',\n",
       "        '#AEE256'\n",
       "    ];\n",
       "    this.colorsByPath = {};\n",
       "    this.colorIndex = 0;\n",
       "    this.lossFuncs = {};\n",
       "\n",
       "    this.isCVinited = false;\n",
       "};\n",
       "\n",
       "/* eslint-disable */\n",
       "CatboostIpython.prototype.loadStyles = function(path, fn, scope) {\n",
       "    $('link[catboost=\"1\"]').remove();\n",
       "\n",
       "    var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes\n",
       "        link = document.createElement('link');           // create the link node\n",
       "    link.setAttribute('href', path);\n",
       "    link.setAttribute('rel', 'stylesheet');\n",
       "    link.setAttribute('type', 'text/css');\n",
       "    link.setAttribute('catboost', '1');\n",
       "\n",
       "    var sheet, cssRules;\n",
       "    // get the correct properties to check for depending on the browser\n",
       "    if ('sheet' in link) {\n",
       "        sheet = 'sheet'; cssRules = 'cssRules';\n",
       "    } else {\n",
       "        sheet = 'styleSheet'; cssRules = 'rules';\n",
       "    }\n",
       "\n",
       "    var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded\n",
       "        try {\n",
       "            if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded\n",
       "                clearInterval(interval_id);                      // clear the counters\n",
       "                clearTimeout(timeout_id);\n",
       "                fn.call( scope || window, true, link);           // fire the callback with success == true\n",
       "            }\n",
       "        } catch(e) {} finally {}\n",
       "    }, 50 ),                                                   // how often to check if the stylesheet is loaded\n",
       "    timeout_id = setTimeout( function() {       // start counting down till fail\n",
       "        clearInterval( interval_id );             // clear the counters\n",
       "        clearTimeout( timeout_id );\n",
       "        head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM\n",
       "        fn.call( scope || window, false, link ); // fire the callback with success == false\n",
       "    }, 15000 );                                 // how long to wait before failing\n",
       "\n",
       "    head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet\n",
       "\n",
       "    return link; // return the link node;\n",
       "};\n",
       "/* eslint-enable */\n",
       "\n",
       "CatboostIpython.prototype.resizeCharts = function() {\n",
       "    // width fix for development\n",
       "    $('.catboost-graph__charts', this.layout).css({width: $('.catboost-graph').width()});\n",
       "\n",
       "    this.plotly.Plots.resize(this.traces[this.activeTab].parent);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addMeta = function(path, meta) {\n",
       "    this.meta[path] = meta;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addLayout = function(parent) {\n",
       "    if (this.layout) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var cvAreaControls = '';\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        cvAreaControls =    '<div>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-cvstddev' + this.index + '\"' + (this.stddevEnabled ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control2-cvstddev' + this.index + '\" class=\"catboost-panel__controls2_label catboost-panel__controls2_label-long\">Standard Deviation</label>' +\n",
       "                            '</div>';\n",
       "    }\n",
       "\n",
       "    this.layout = $('<div class=\"catboost\">' +\n",
       "                        '<div class=\"catboost-panel\">' +\n",
       "                            '<div class=\"catboost-panel__controls\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-learn' + this.index + '\" ' + (!this.layoutDisabled.learn ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-learn' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_learn_pic\" style=\"border-color:#999\"></div>Learn</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__controls_checkbox\" id=\"catboost-control-test' + this.index + '\" ' + (!this.layoutDisabled.test ? ' checked=\"checked\"' : '') + '></input>' +\n",
       "                                '<label for=\"catboost-control-test' + this.index + '\" class=\"catboost-panel__controls_label\"><div class=\"catboost-panel__serie_test_pic\" style=\"border-color:#999\"></div>Eval</label>' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '\">' +\n",
       "                            '</div>' +\n",
       "                            '<div class=\"catboost-panel__controls2\">' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-clickmode' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-clickmode' + this.index + '\" class=\"catboost-panel__controls2_label\">Click Mode</label>' +\n",
       "                                '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-log' + this.index + '\"></input>' +\n",
       "                                '<label for=\"catboost-control2-log' + this.index + '\" class=\"catboost-panel__controls2_label\">Logarithm</label>' +\n",
       "                                '<div>' +\n",
       "                                    '<input type=\"checkbox\" class=\"catboost-panel__control_checkbox\" id=\"catboost-control2-smooth' + this.index + '\"></input>' +\n",
       "                                    '<label for=\"catboost-control2-smooth' + this.index + '\" class=\"catboost-panel__controls2_label\">Smooth</label>' +\n",
       "                                    '<input id=\"catboost-control2-slider' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slider\" type =\"range\" value=\"0\" min=\"0\" max=\"1\" step =\"0.01\" for=\"rangeInputValue\" name=\"rangeInput\"/>' +\n",
       "                                    '<input id=\"catboost-control2-slidervalue' + this.index + '\" disabled=\"disabled\" class=\"catboost-panel__control_slidervalue\" value=\"0\" min=\"0\" max=\"1\" for=\"rangeInput\" name=\"rangeInputValue\"/>' +\n",
       "                                '</div>' +\n",
       "                                cvAreaControls +\n",
       "                            '</div>' +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-graph\">' +\n",
       "                            '<div class=\"catboost-graph__tabs\"></div>' +\n",
       "                            '<div class=\"catboost-graph__charts\"></div>' +\n",
       "                        '</div>' +\n",
       "                    '</div>');\n",
       "    $(parent).append(this.layout);\n",
       "\n",
       "    this.addTabEvents();\n",
       "    this.addControlEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTabEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-graph__tabs', this.layout).click(function(e) {\n",
       "        if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var id = $(e.target).attr('tabid');\n",
       "\n",
       "        self.activeTab = id;\n",
       "\n",
       "        $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');\n",
       "\n",
       "        $('.catboost-graph__tab[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__tab_active');\n",
       "        $('.catboost-graph__chart[tabid=\"' + id + '\"]', self.layout).addClass('catboost-graph__chart_active');\n",
       "\n",
       "        self.cleanSeries();\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "        self.resizeCharts();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addControlEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('#catboost-control-learn' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.learn = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control-test' + this.index, this.layout).click(function() {\n",
       "        self.layoutDisabled.test = !$(this)[0].checked;\n",
       "\n",
       "        $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-clickmode' + this.index, this.layout).click(function() {\n",
       "        self.clickMode = $(this)[0].checked;\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-log' + this.index, this.layout).click(function() {\n",
       "        self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';\n",
       "\n",
       "        self.forEveryLayout(function(layout) {\n",
       "            layout.yaxis = {type: self.logarithmMode};\n",
       "        });\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    var slider = $('#catboost-control2-slider' + this.index),\n",
       "        sliderValue = $('#catboost-control2-slidervalue' + this.index);\n",
       "\n",
       "    $('#catboost-control2-smooth' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setSmoothness(enabled ? self.lastSmooth : -1);\n",
       "\n",
       "        slider.prop('disabled', !enabled);\n",
       "        sliderValue.prop('disabled', !enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    $('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {\n",
       "        var enabled = $(this)[0].checked;\n",
       "\n",
       "        self.setStddev(enabled);\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    slider.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        sliderValue.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "\n",
       "    sliderValue.on('input change', function() {\n",
       "        var smooth = Number($(this).val());\n",
       "\n",
       "        slider.val(isNaN(smooth) ? 0 : smooth);\n",
       "\n",
       "        self.setSmoothness(smooth);\n",
       "        self.lastSmooth = smooth;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {\n",
       "    if (trace) {\n",
       "        trace.visible = visibility;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesVisibility = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        traces,\n",
       "        smoothDisabled = this.getSmoothness() === -1,\n",
       "        self = this;\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            traces = tracesHash[train].traces;\n",
       "\n",
       "            if (this.layoutDisabled.traces[train]) {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, false);\n",
       "                });\n",
       "            } else {\n",
       "                traces.forEach(function(trace) {\n",
       "                    self.setTraceVisibility(trace, true);\n",
       "                });\n",
       "\n",
       "                if (this.hasCVMode) {\n",
       "                    if (this.stddevEnabled) {\n",
       "                        self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, true);\n",
       "                        });\n",
       "                    } else {\n",
       "                        self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "\n",
       "                        self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {\n",
       "                            self.setTraceVisibility(trace, false);\n",
       "                        });\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                if (smoothDisabled) {\n",
       "                    self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['learn']) {\n",
       "                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "\n",
       "                if (this.layoutDisabled['test']) {\n",
       "                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {\n",
       "                        self.setTraceVisibility(trace, false);\n",
       "                    });\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getSmoothness = function() {\n",
       "    return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setSmoothness = function(weight) {\n",
       "    if (weight < 0 && weight !== -1 || weight > 1) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    this.smoothness = weight;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.setStddev = function(enabled) {\n",
       "    this.stddevEnabled = enabled;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawActiveChart = function() {\n",
       "    this.chartsToRedraw[this.activeTab] = true;\n",
       "\n",
       "    this.redrawAll();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redraw = function() {\n",
       "    if (this.chartsToRedraw[this.activeTab]) {\n",
       "        this.chartsToRedraw[this.activeTab] = false;\n",
       "\n",
       "        this.updateTracesVisibility();\n",
       "        this.updateTracesCV();\n",
       "        this.updateTracesBest();\n",
       "        this.updateTracesValues();\n",
       "        this.updateTracesSmoothness();\n",
       "\n",
       "        this.plotly.redraw(this.traces[this.activeTab].parent);\n",
       "    }\n",
       "\n",
       "    this.drawTraces();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addRedrawFunc = function() {\n",
       "    this.redrawFunc = throttle(this.redraw, 400, false, this);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.redrawAll = function() {\n",
       "    if (!this.redrawFunc) {\n",
       "        this.addRedrawFunc();\n",
       "    }\n",
       "\n",
       "    this.redrawFunc();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addPoints = function(parent, data) {\n",
       "    var self = this;\n",
       "\n",
       "    data.chunks.forEach(function(item) {\n",
       "        if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {\n",
       "            if (!self.timeLeft[data.path]) {\n",
       "                self.timeLeft[data.path] = [];\n",
       "            }\n",
       "\n",
       "            self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];\n",
       "        }\n",
       "\n",
       "        ['test', 'learn'].forEach(function(type) {\n",
       "            var sets = self.meta[data.path][type + '_sets'],\n",
       "                metrics = self.meta[data.path][type + '_metrics'];\n",
       "\n",
       "            for (var i = 0; i < metrics.length; i++) {\n",
       "                var nameOfMetric = metrics[i].name,\n",
       "                    cvAdded = false;\n",
       "                    hovertextParametersAdded = false;\n",
       "\n",
       "                self.lossFuncs[nameOfMetric] = metrics[i].best_value;\n",
       "\n",
       "                for (var j = 0; j < sets.length; j++) {\n",
       "                    var nameOfSet = sets[j],\n",
       "                        params = {\n",
       "                            chartName: nameOfMetric,\n",
       "                            index: i,\n",
       "                            train: data.train,\n",
       "                            type: type,\n",
       "                            path: data.path,\n",
       "                            indexOfSet: j,\n",
       "                            nameOfSet: nameOfSet\n",
       "                        },\n",
       "                        key = self.getKey(params),\n",
       "                        launchMode = self.getLaunchMode(data.path);\n",
       "\n",
       "                    if (!self.activeTab) {\n",
       "                        self.activeTab = key.chartId;\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' ) {\n",
       "                        // we need to set launch mode before first getTrace call\n",
       "                        self.hasCVMode = true;\n",
       "\n",
       "                        if (!self.isCVinited) {\n",
       "                            // and we don't need to reset setting for next iterations\n",
       "                            self.layoutDisabled.learn = true;\n",
       "                            self.setStddev(true);\n",
       "\n",
       "                            self.isCVinited = true;\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    var valuesOfSet = item[nameOfSet],\n",
       "                        pointValue = valuesOfSet[i],\n",
       "                        pointIndex = item.iteration,\n",
       "                        // traces\n",
       "                        trace = self.getTrace(parent, params),\n",
       "                        smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),\n",
       "                        bestValueTrace = null;\n",
       "\n",
       "                    if (type === 'test') {\n",
       "                        if (launchMode !== 'CV') {\n",
       "                            self.getTrace(parent, $.extend({best_point: true}, params));\n",
       "                        }\n",
       "\n",
       "                        if (typeof self.lossFuncs[nameOfMetric] === 'number') {\n",
       "                            bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if (pointValue !== 'inf' && pointValue !== 'nan') {\n",
       "                        trace.x[pointIndex] = pointIndex;\n",
       "                        trace.y[pointIndex] = valuesOfSet[i];\n",
       "                        trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);\n",
       "                        if (item.hasOwnProperty('parameters')) {\n",
       "                            self.hovertextParameters[pointIndex] = '';\n",
       "                            for (var parameter in item.parameters[0]) {\n",
       "                                if (item.parameters[0].hasOwnProperty(parameter)) {\n",
       "                                    valueOfParameter = item.parameters[0][parameter];\n",
       "                                    self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;\n",
       "                                }\n",
       "                            }\n",
       "                        }\n",
       "                        if (!hovertextParametersAdded && type === 'test') {\n",
       "                            hovertextParametersAdded = true;\n",
       "                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];\n",
       "                        }\n",
       "                        smoothedTrace.x[pointIndex] = pointIndex;\n",
       "                    }\n",
       "\n",
       "                    if (bestValueTrace) {\n",
       "                        bestValueTrace.x[pointIndex] = pointIndex;\n",
       "                        bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];\n",
       "                    }\n",
       "\n",
       "                    if (launchMode === 'CV' && !cvAdded) {\n",
       "                        cvAdded = true;\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));\n",
       "\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true}, params));\n",
       "                        self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));\n",
       "\n",
       "                        if (type === 'test') {\n",
       "                            self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                self.chartsToRedraw[key.chartId] = true;\n",
       "\n",
       "                self.redrawAll();\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getLaunchMode = function(path) {\n",
       "    return this.meta[path].launch_mode;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartNode = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__chart\" tabid=\"' + params.id + '\"></div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__chart_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChartTab = function(params, active) {\n",
       "    var node = $('<div class=\"catboost-graph__tab\" tabid=\"' + params.id + '\">' + params.name + '</div>');\n",
       "\n",
       "    if (active) {\n",
       "        node.addClass('catboost-graph__tab_active');\n",
       "    }\n",
       "\n",
       "    return node;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryChart = function(callback) {\n",
       "    for (var name in this.traces) {\n",
       "        if (this.traces.hasOwnProperty(name)) {\n",
       "            callback(this.traces[name]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.forEveryLayout = function(callback) {\n",
       "    this.forEveryChart(function(chart) {\n",
       "        callback(chart.layout);\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getChart = function(parent, params) {\n",
       "    var id = params.id,\n",
       "        self = this;\n",
       "\n",
       "    if (this.charts[id]) {\n",
       "        return this.charts[id];\n",
       "    }\n",
       "\n",
       "    this.addLayout(parent);\n",
       "\n",
       "    var active = this.activeTab === params.id,\n",
       "        chartNode = this.getChartNode(params, active),\n",
       "        chartTab = this.getChartTab(params, active);\n",
       "\n",
       "    $('.catboost-graph__charts', this.layout).append(chartNode);\n",
       "    $('.catboost-graph__tabs', this.layout).append(chartTab);\n",
       "\n",
       "    this.traces[id] = {\n",
       "        id: params.id,\n",
       "        name: params.name,\n",
       "        parent: chartNode[0],\n",
       "        traces: [],\n",
       "        layout: {\n",
       "            xaxis: {\n",
       "                range: [0, Number(this.meta[params.path].iteration_count)],\n",
       "                type: 'linear',\n",
       "                tickmode: 'auto',\n",
       "                showspikes: true,\n",
       "                spikethickness: 1,\n",
       "                spikedash: 'longdashdot',\n",
       "                spikemode: 'across',\n",
       "                zeroline: false,\n",
       "                showgrid: false\n",
       "            },\n",
       "            yaxis: {\n",
       "                zeroline: false\n",
       "                //showgrid: false\n",
       "                //hoverformat : '.7f'\n",
       "            },\n",
       "            separators: '. ',\n",
       "            //hovermode: 'x',\n",
       "            margin: {l: 38, r: 0, t: 35, b: 30},\n",
       "            autosize: true,\n",
       "            showlegend: false\n",
       "        },\n",
       "        options: {\n",
       "            scrollZoom: false,\n",
       "            modeBarButtonsToRemove: ['toggleSpikelines'],\n",
       "            displaylogo: false\n",
       "        }\n",
       "    };\n",
       "\n",
       "    this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);\n",
       "\n",
       "    chartNode[0].on('plotly_hover', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x);\n",
       "    });\n",
       "\n",
       "    chartNode[0].on('plotly_click', function(e) {\n",
       "        self.updateTracesValues(e.points[0].x, true);\n",
       "    });\n",
       "\n",
       "    return this.charts[id];\n",
       "};\n",
       "\n",
       "\n",
       "CatboostIpython.prototype.getTrace = function(parent, params) {\n",
       "    var key = this.getKey(params),\n",
       "        chartSeries = [];\n",
       "\n",
       "    if (this.traces[key.chartId]) {\n",
       "        chartSeries = this.traces[key.chartId].traces.filter(function(trace) {\n",
       "            return trace.name === key.traceName;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    if (chartSeries.length) {\n",
       "        return chartSeries[0];\n",
       "    } else {\n",
       "        this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});\n",
       "\n",
       "        var plotParams = {\n",
       "                color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),\n",
       "                fillsmoothcolor: this.getNextColor(params.path, 0.1),\n",
       "                fillcolor: this.getNextColor(params.path, 0.4),\n",
       "                hoverinfo: params.cv_avg ? 'skip' : 'text+x',\n",
       "                width: params.cv_avg ? 2 : 1,\n",
       "                dash: params.type === 'test' ? 'solid' : 'dot'\n",
       "            },\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                hovertext: [],\n",
       "                hoverinfo: plotParams.hoverinfo,\n",
       "                line: {\n",
       "                    width: plotParams.width,\n",
       "                    dash: plotParams.dash,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                hoveron: 'points',\n",
       "                connectgaps: true\n",
       "            };\n",
       "\n",
       "        if (params.best_point) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                marker: {\n",
       "                    width: 2,\n",
       "                    color: plotParams.color\n",
       "                },\n",
       "                hovertext: [],\n",
       "                hoverinfo: 'text',\n",
       "                mode: 'markers',\n",
       "                type: 'scatter'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.best_value) {\n",
       "            trace = {\n",
       "                name: key.traceName,\n",
       "                _params: params,\n",
       "                x: [],\n",
       "                y: [],\n",
       "                line: {\n",
       "                    width: 1,\n",
       "                    dash: 'dash',\n",
       "                    color: '#CCCCCC'\n",
       "                },\n",
       "                mode: 'lines',\n",
       "                connectgaps: true,\n",
       "                hoverinfo: 'skip'\n",
       "            };\n",
       "        }\n",
       "\n",
       "        if (params.cv_stddev_last) {\n",
       "            trace.fill = 'tonexty';\n",
       "        }\n",
       "\n",
       "        trace._params.plotParams = plotParams;\n",
       "\n",
       "        this.traces[key.chartId].traces.push(trace);\n",
       "\n",
       "        return trace;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getKey = function(params) {\n",
       "    var traceName = [\n",
       "        params.train,\n",
       "        params.type,\n",
       "        params.indexOfSet,\n",
       "        (params.smoothed ? 'smoothed' : ''),\n",
       "        (params.best_point ? 'best_pount' : ''),\n",
       "        (params.best_value ? 'best_value' : ''),\n",
       "        (params.cv_avg ? 'cv_avg' : ''),\n",
       "        (params.cv_stddev_first ? 'cv_stddev_first' : ''),\n",
       "        (params.cv_stddev_last ? 'cv_stddev_last' : '')\n",
       "    ].join(';');\n",
       "\n",
       "    return {\n",
       "        chartId: params.chartName,\n",
       "        traceName: traceName,\n",
       "        colorId: params.train\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] !== trace._params[prop]) {\n",
       "                    return false;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return true;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.filterTracesOne = function(traces, filter) {\n",
       "    traces = traces || this.traces[this.activeTab].traces;\n",
       "\n",
       "    return traces.filter(function(trace) {\n",
       "        for (var prop in filter) {\n",
       "            if (filter.hasOwnProperty(prop)) {\n",
       "                if (filter[prop] === trace._params[prop]) {\n",
       "                    return true;\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return false;\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cleanSeries = function() {\n",
       "    $('.catboost-panel__series', this.layout).html('');\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.groupTraces = function() {\n",
       "    var traces = this.traces[this.activeTab].traces,\n",
       "        index = 0,\n",
       "        tracesHash = {};\n",
       "\n",
       "    traces.map(function(trace) {\n",
       "        var train = trace._params.train;\n",
       "\n",
       "        if (!tracesHash[train]) {\n",
       "            tracesHash[train] = {\n",
       "                index: index,\n",
       "                traces: [],\n",
       "                info: {\n",
       "                    path: trace._params.path,\n",
       "                    color: trace._params.plotParams.color\n",
       "                }\n",
       "            };\n",
       "\n",
       "            index++;\n",
       "        }\n",
       "\n",
       "        tracesHash[train].traces.push(trace);\n",
       "    });\n",
       "\n",
       "    return tracesHash;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTraces = function() {\n",
       "    if ($('.catboost-panel__series .catboost-panel__serie', this.layout).length) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var html = '',\n",
       "        tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train)) {\n",
       "            html += this.drawTrace(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "\n",
       "    $('.catboost-panel__series', this.layout).html(html);\n",
       "\n",
       "    this.updateTracesValues();\n",
       "\n",
       "    this.addTracesEvents();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getTraceDefParams = function(params) {\n",
       "    var defParams =  {\n",
       "        smoothed: undefined,\n",
       "        best_point: undefined,\n",
       "        best_value: undefined,\n",
       "        cv_avg: undefined,\n",
       "        cv_stddev_first: undefined,\n",
       "        cv_stddev_last: undefined\n",
       "    };\n",
       "\n",
       "    if (params) {\n",
       "        return $.extend(defParams, params);\n",
       "    } else {\n",
       "        return defParams;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.drawTrace = function(train, hash) {\n",
       "    var info = hash.info,\n",
       "        id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        items = {\n",
       "            learn: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            },\n",
       "            test: {\n",
       "                middle: '',\n",
       "                bottom: ''\n",
       "            }\n",
       "        },\n",
       "        tracesNames = '';\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            items[type].middle += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_' + type + '_value\"></div>';\n",
       "\n",
       "            items[type].bottom += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:transparent\"></div>' +\n",
       "                                  '<div data-index=\"' + trace._params.indexOfSet + '\" class=\"catboost-panel__serie_best_' + type + '_value\"></div>';\n",
       "\n",
       "            tracesNames += '<div class=\"catboost-panel__serie_' + type + '_pic\" style=\"border-color:' + info.color + '\"></div>' +\n",
       "                           '<div class=\"catboost-panel__serie_' + type + '_name\">' + trace._params.nameOfSet + '</div>';\n",
       "        });\n",
       "    });\n",
       "\n",
       "    var timeSpendHtml = '<div class=\"catboost-panel__serie_time\">' +\n",
       "                             '<div class=\"catboost-panel__serie_time_spend\" title=\"Time spend\"></div>' +\n",
       "                        '</div>';\n",
       "\n",
       "    var html = '<div id=\"' + id + '\" class=\"catboost-panel__serie\" style=\"color:' + info.color + '\">' +\n",
       "                    '<div class=\"catboost-panel__serie_top\">' +\n",
       "                        '<input type=\"checkbox\" data-seriename=\"' + train + '\" class=\"catboost-panel__serie_checkbox\" id=\"' + id + '-box\" ' + (!this.layoutDisabled.series[train] ? 'checked=\"checked\"' : '') + '></input>' +\n",
       "                        '<label title=' + this.meta[info.path].name + ' for=\"' + id + '-box\" class=\"catboost-panel__serie_label\">' + train + '<div class=\"catboost-panel__serie_time_left\" title=\"Estimate time\"></div></label>' +\n",
       "                        (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +\n",
       "                    '</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__learn_hint\">curr</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_hint catboost-panel__serie__test_hint\">best</div>' +\n",
       "                    '<div class=\"catboost-panel__serie_iteration\" title=\"curr iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_best_iteration\" title=\"best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration\"></div>' +\n",
       "                    '<div class=\"catboost-panel__serie_scroll\">' +\n",
       "                        '<div class=\"catboost-panel__serie_names\">' +\n",
       "                            tracesNames +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_middle\">' +\n",
       "                            items.learn.middle +\n",
       "                            items.test.middle +\n",
       "                        '</div>' +\n",
       "                        '<div class=\"catboost-panel__serie_bottom\">' +\n",
       "                            items.learn.bottom +\n",
       "                            items.test.bottom +\n",
       "                        '</div>' +\n",
       "                    '</div>' +\n",
       "                '</div>';\n",
       "\n",
       "    return html;\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesValues = function(iteration, click) {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceValues(train, tracesHash[train], iteration, click);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesBest = function() {\n",
       "    var tracesHash = this.groupTraces();\n",
       "\n",
       "    for (var train in tracesHash) {\n",
       "        if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {\n",
       "            this.updateTraceBest(train, tracesHash[train]);\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getBestValue = function(data) {\n",
       "    if (!data.length) {\n",
       "        return {\n",
       "            best: undefined,\n",
       "            index: -1\n",
       "        };\n",
       "    }\n",
       "\n",
       "    var best = data[0],\n",
       "        index = 0,\n",
       "        func = this.lossFuncs[this.traces[this.activeTab].name],\n",
       "        bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;\n",
       "\n",
       "    for (var i = 1, l = data.length; i < l; i++) {\n",
       "        if (func === 'Min' && data[i] < best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (func === 'Max' && data[i] > best) {\n",
       "            best = data[i];\n",
       "            index = i;\n",
       "        }\n",
       "\n",
       "        if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {\n",
       "            best = data[i];\n",
       "            bestDiff = Math.abs(data[i] - func);\n",
       "            index = i;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return {\n",
       "        best: best,\n",
       "        index: index,\n",
       "        func: func\n",
       "    };\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCV = function() {\n",
       "    this.updateTracesCVAvg();\n",
       "\n",
       "    if (this.hasCVMode && this.stddevEnabled) {\n",
       "        this.updateTracesCVStdDev();\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVAvg = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({\n",
       "            cv_avg: true\n",
       "        })),\n",
       "        self = this;\n",
       "\n",
       "    avgTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length) {\n",
       "            self.cvAvgFunc(origTraces, trace);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (var i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (var j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count > 0) {\n",
       "            avgTrace.x[i] = i;\n",
       "            avgTrace.y[i] = sum / count;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesCVStdDev = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),\n",
       "        self = this;\n",
       "\n",
       "    firstTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed\n",
       "            })),\n",
       "            lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                smoothed: trace._params.smoothed,\n",
       "                cv_stddev_last: true\n",
       "            }));\n",
       "\n",
       "        if (origTraces.length && lastTraces.length === 1) {\n",
       "            self.cvStdDevFunc(origTraces, trace, lastTraces[0]);\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {\n",
       "    var maxCount = origTraces.length,\n",
       "        maxLength = -1,\n",
       "        count,\n",
       "        sum,\n",
       "        i, j;\n",
       "\n",
       "    origTraces.forEach(function(origTrace) {\n",
       "        if (origTrace.y.length > maxLength) {\n",
       "            maxLength = origTrace.y.length;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    for (i = 0; i < maxLength; i++) {\n",
       "        sum = 0;\n",
       "        count = 0;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                sum += origTraces[j].y[i];\n",
       "                count++;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        if (count <= 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var std = 0,\n",
       "            avg = sum / count;\n",
       "\n",
       "        for (j = 0; j < maxCount; j++) {\n",
       "            if (typeof origTraces[j].y[i] !== 'undefined') {\n",
       "                std += Math.pow(origTraces[j].y[i] - avg, 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "        std /= (count - 1);\n",
       "        std = Math.pow(std, 0.5);\n",
       "\n",
       "        firstTrace.x[i] = i;\n",
       "        firstTrace.y[i] = avg - std;\n",
       "        firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);\n",
       "        lastTrace.x[i] = i;\n",
       "        lastTrace.y[i] = avg + std;\n",
       "        lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);\n",
       "        if (this.hovertextParameters.length > i) {\n",
       "            firstTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "            lastTrace.hovertext[i] += this.hovertextParameters[i];\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTracesSmoothness = function() {\n",
       "    var tracesHash = this.groupTraces(),\n",
       "        smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),\n",
       "        enabled = this.getSmoothness() > -1,\n",
       "        self = this;\n",
       "\n",
       "    smoothedTraces.forEach(function(trace) {\n",
       "        var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: trace._params.type,\n",
       "                indexOfSet: trace._params.indexOfSet,\n",
       "                cv_avg: trace._params.cv_avg,\n",
       "                cv_stddev_first: trace._params.cv_stddev_first,\n",
       "                cv_stddev_last: trace._params.cv_stddev_last\n",
       "            })),\n",
       "            colorFlag = false;\n",
       "\n",
       "        if (origTraces.length === 1) {\n",
       "            origTraces = origTraces[0];\n",
       "\n",
       "            if (origTraces.visible) {\n",
       "                if (enabled) {\n",
       "                    self.smoothFunc(origTraces, trace);\n",
       "                    colorFlag = true;\n",
       "                }\n",
       "\n",
       "                self.highlightSmoothedTrace(origTraces, trace, colorFlag);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {\n",
       "    if (flag) {\n",
       "        smoothedTrace.line.color = trace._params.plotParams.color;\n",
       "        trace.line.color = smoothedTrace._params.plotParams.color;\n",
       "        trace.hoverinfo = 'skip';\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillsmoothcolor;\n",
       "        }\n",
       "    } else {\n",
       "        trace.line.color = trace._params.plotParams.color;\n",
       "        trace.hoverinfo = trace._params.plotParams.hoverinfo;\n",
       "\n",
       "        if (trace._params.cv_stddev_last) {\n",
       "            trace.fillcolor = trace._params.plotParams.fillcolor;\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {\n",
       "    var data = origTrace.y,\n",
       "        smoothedPoints = this.smooth(data, this.getSmoothness()),\n",
       "        smoothedIndex = 0,\n",
       "        self = this;\n",
       "\n",
       "    if (smoothedPoints.length) {\n",
       "        data.forEach(function (d, index) {\n",
       "            if (!smoothedTrace.x[index]) {\n",
       "                smoothedTrace.x[index] = index;\n",
       "            }\n",
       "\n",
       "            var nameOfSet = smoothedTrace._params.nameOfSet;\n",
       "\n",
       "            if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {\n",
       "                nameOfSet = smoothedTrace._params.type + ' std';\n",
       "            }\n",
       "\n",
       "            smoothedTrace.y[index] = smoothedPoints[smoothedIndex];\n",
       "            smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);\n",
       "            if (self.hovertextParameters.length > index) {\n",
       "                smoothedTrace.hovertext[index] += self.hovertextParameters[index];\n",
       "            }\n",
       "            smoothedIndex++;\n",
       "        });\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {\n",
       "    if (typeof value === 'undefined') {\n",
       "        return '';\n",
       "    }\n",
       "\n",
       "    suffix = suffix || '';\n",
       "\n",
       "    return '<span title=\"' + suffix + 'value ' + value + '\">' + value + '</span>';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceBest = function(train, hash) {\n",
       "    var traces = this.filterTracesOne(hash.traces, {best_point: true}),\n",
       "        self = this;\n",
       "\n",
       "    traces.forEach(function(trace) {\n",
       "        var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                indexOfSet: trace._params.indexOfSet\n",
       "            }));\n",
       "\n",
       "        if (self.hasCVMode) {\n",
       "            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                train: trace._params.train,\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            }));\n",
       "        }\n",
       "\n",
       "        var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "        if (bestValue.index !== -1) {\n",
       "            trace.x[0] = bestValue.index;\n",
       "            trace.y[0] = bestValue.best;\n",
       "            trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;\n",
       "        }\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {\n",
       "    var id = 'catboost-serie-' + this.index + '-' + hash.index,\n",
       "        traces = {\n",
       "            learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'learn'})),\n",
       "            test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: 'test'}))\n",
       "        },\n",
       "        path = hash.info.path,\n",
       "        self = this;\n",
       "\n",
       "    ['learn', 'test'].forEach(function(type) {\n",
       "        traces[type].forEach(function(trace) {\n",
       "            var data = trace.y || [],\n",
       "                index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,\n",
       "                value = data.length ? data[index] : undefined,\n",
       "                testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({\n",
       "                    type: 'test',\n",
       "                    indexOfSet: trace._params.indexOfSet\n",
       "                })),\n",
       "                bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),\n",
       "                timeLeft = '',\n",
       "                timeSpend = '';\n",
       "\n",
       "            if (click || !self.clickMode) {\n",
       "                $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(value, index, type + ' '));\n",
       "                $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {\n",
       "                    timeLeft = self.timeLeft[path][data.length - 1][0];\n",
       "                }\n",
       "                $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');\n",
       "\n",
       "                if (self.timeLeft[path] && self.timeLeft[path][index]) {\n",
       "                    timeSpend = self.timeLeft[path][index][1];\n",
       "                }\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));\n",
       "                $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "\n",
       "                $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)\n",
       "                    .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "\n",
       "    if (this.hasCVMode) {\n",
       "        var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({\n",
       "                type: 'test',\n",
       "                cv_avg: true\n",
       "            })),\n",
       "            bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);\n",
       "\n",
       "            $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');\n",
       "    }\n",
       "\n",
       "    if (click) {\n",
       "        this.clickMode = true;\n",
       "\n",
       "        $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;\n",
       "    }\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.addTracesEvents = function() {\n",
       "    var self = this;\n",
       "\n",
       "    $('.catboost-panel__serie_checkbox', this.layout).click(function() {\n",
       "        var name = $(this).data('seriename');\n",
       "\n",
       "        self.layoutDisabled.traces[name] = !$(this)[0].checked;\n",
       "\n",
       "        self.redrawActiveChart();\n",
       "    });\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.getNextColor = function(path, opacity) {\n",
       "    var color;\n",
       "\n",
       "    if (this.colorsByPath[path]) {\n",
       "        color = this.colorsByPath[path];\n",
       "    } else {\n",
       "        color = this.colors[this.colorIndex];\n",
       "        this.colorsByPath[path] = color;\n",
       "\n",
       "        this.colorIndex++;\n",
       "\n",
       "        if (this.colorIndex > this.colors.length - 1) {\n",
       "            this.colorIndex = 0;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    return this.hexToRgba(color, opacity);\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.hexToRgba = function(value, opacity) {\n",
       "    if (value.length < 6) {\n",
       "        var pattern = /^#?([a-f\\d])([a-f\\d])([a-f\\d])/i;\n",
       "        value = value.replace(pattern, function(m, r, g, b) {\n",
       "            return '#' + r + r + g + g + b + b;\n",
       "        });\n",
       "    }\n",
       "\n",
       "    var result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})/i.exec(value);\n",
       "    var rgb = {\n",
       "        r: parseInt(result[1], 16),\n",
       "        g: parseInt(result[2], 16),\n",
       "        b: parseInt(result[3], 16)\n",
       "    };\n",
       "\n",
       "    return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')';\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.convertTime = function(time) {\n",
       "    if (!time) {\n",
       "        return '0s';\n",
       "    }\n",
       "\n",
       "    time = Math.floor(time * 1000);\n",
       "\n",
       "    var millis = time % 1000;\n",
       "    time = parseInt(time / 1000, 10);\n",
       "    var seconds = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var minutes = time % 60;\n",
       "    time = parseInt(time / 60, 10);\n",
       "    var hours = time % 24;\n",
       "    var out = \"\";\n",
       "    if (hours && hours > 0) {\n",
       "        out += hours + 'h ';\n",
       "        seconds = 0;\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (minutes && minutes > 0) {\n",
       "        out += minutes + 'm ';\n",
       "        millis = 0;\n",
       "    }\n",
       "    if (seconds && seconds > 0) {\n",
       "        out += seconds + 's ';\n",
       "    }\n",
       "    if (millis && millis > 0) {\n",
       "        out += millis + 'ms';\n",
       "    }\n",
       "\n",
       "    return out.trim();\n",
       "};\n",
       "\n",
       "CatboostIpython.prototype.mean = function(values, valueof) {\n",
       "    var n = values.length,\n",
       "        m = n,\n",
       "        i = -1,\n",
       "        value,\n",
       "        sum = 0,\n",
       "        number = function(x) {\n",
       "            return x === null ? NaN : +x;\n",
       "        };\n",
       "\n",
       "    if (valueof === null) {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(values[i]))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    } else {\n",
       "        while (++i < n) {\n",
       "            if (!isNaN(value = number(valueof(values[i], i, values)))) {\n",
       "                sum += value;\n",
       "            } else {\n",
       "                --m;\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (m) {\n",
       "        return sum / m;\n",
       "    }\n",
       "};\n",
       "\n",
       "// from TensorBoard\n",
       "CatboostIpython.prototype.smooth = function(data, weight) {\n",
       "    // When increasing the smoothing window, it smoothes a lot with the first\n",
       "    // few points and then starts to gradually smooth slower, so using an\n",
       "    // exponential function makes the slider more consistent. 1000^x has a\n",
       "    // range of [1, 1000], so subtracting 1 and dividing by 999 results in a\n",
       "    // range of [0, 1], which can be used as the percentage of the data, so\n",
       "    // that the kernel size can be specified as a percentage instead of a\n",
       "    // hardcoded number, what would be bad with multiple series.\n",
       "    var factor = (Math.pow(1000, weight) - 1) / 999,\n",
       "        kernelRadius = Math.floor(data.length * factor / 2),\n",
       "        res = [],\n",
       "        self = this;\n",
       "\n",
       "    data.forEach(function (d, i) {\n",
       "        var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);\n",
       "        var start = i - actualKernelRadius;\n",
       "        var end = i + actualKernelRadius + 1;\n",
       "        var point = d;\n",
       "        // Only smooth finite numbers.\n",
       "        if (!isFinite(point)) {\n",
       "            res.push(point);\n",
       "        } else {\n",
       "            res.push(self.mean(data.slice(start, end).filter(function(d) {\n",
       "                return isFinite(d);\n",
       "            }), null));\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return res;\n",
       "};\n",
       "var debug = false;\n",
       "\n",
       "if (debug) {\n",
       "    require.config({\n",
       "        shim:{\n",
       "            \"custom/CatboostIpythonPlotly\":{\n",
       "                deps:[\"custom/plotly-basic.min\"]\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "\n",
       "    require.undef('catboost_module');\n",
       "    require.undef('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "var moduleBase = '@jupyter-widgets/base';\n",
       "var modules = [moduleBase];\n",
       "\n",
       "if (debug) {\n",
       "    modules.push('custom/CatboostIpythonPlotly');\n",
       "}\n",
       "\n",
       "define('catboost_module', modules, function(widgets) {\n",
       "    var getInstance = function(el) {\n",
       "            var id = $(el).attr('catboost-id');\n",
       "\n",
       "            if (!id) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            id = id.replace('catboost_', '');\n",
       "\n",
       "            if (!window.catboostIpythonInstances[id]) {\n",
       "                return null;\n",
       "            }\n",
       "\n",
       "            return window.catboostIpythonInstances[id];\n",
       "        },\n",
       "        addInstance = function(el) {\n",
       "            $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);\n",
       "\n",
       "            var catboostIpython = new CatboostIpython();\n",
       "            catboostIpython.index = window.catboostIpythonIndex;\n",
       "            catboostIpython.plotly = window.Plotly;\n",
       "            if (debug) {\n",
       "                catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})\n",
       "            }\n",
       "\n",
       "            window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;\n",
       "\n",
       "            window.catboostIpythonIndex++;\n",
       "\n",
       "            return catboostIpython;\n",
       "        };\n",
       "\n",
       "    var CatboostIpythonWidget = widgets.DOMWidgetView.extend({\n",
       "\n",
       "        initialize: function() {\n",
       "            CatboostIpythonWidget.__super__.initialize.apply(this, arguments);\n",
       "\n",
       "            if (!window.catboostIpythonInstances) {\n",
       "                window.catboostIpythonInstances = {};\n",
       "            }\n",
       "\n",
       "            if (typeof window.catboostIpythonIndex === 'undefined') {\n",
       "                window.catboostIpythonIndex = 0;\n",
       "            }\n",
       "\n",
       "            var catboostIpythonInstance = getInstance(this.el);\n",
       "\n",
       "            if (!catboostIpythonInstance) {\n",
       "                catboostIpythonInstance = addInstance(this.el);\n",
       "            }\n",
       "\n",
       "            catboostIpythonInstance.init();\n",
       "        },\n",
       "\n",
       "        render: function() {\n",
       "            this.value_changed();\n",
       "            this.model.on('change:value', this.value_changed, this);\n",
       "        },\n",
       "\n",
       "        update: function() {\n",
       "            this.value_changed();\n",
       "        },\n",
       "\n",
       "        value_changed: function() {\n",
       "            this.el.style['width'] = this.model.get('width');\n",
       "            this.el.style['height'] = this.model.get('height');\n",
       "            this.displayed.then(_.bind(this.render_charts, this));\n",
       "        },\n",
       "\n",
       "        process_all: function(parent, params) {\n",
       "            var data = params.data;\n",
       "\n",
       "            for (var path in data) {\n",
       "                if (data.hasOwnProperty(path)) {\n",
       "                    this.process_row(parent, data[path])\n",
       "                }\n",
       "            }\n",
       "        },\n",
       "\n",
       "        process_row: function(parent, data) {\n",
       "            var catboostIpython = getInstance(parent),\n",
       "                path = data.path,\n",
       "                content = data.content,\n",
       "                items = content.data.iterations,\n",
       "                firstIndex = 0,\n",
       "                chunks = [];\n",
       "\n",
       "            if (!items || !items.length) {\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            if (!catboostIpython.lastIndex) {\n",
       "                catboostIpython.lastIndex = {}\n",
       "            }\n",
       "\n",
       "            if (catboostIpython.lastIndex[path]) {\n",
       "                firstIndex = catboostIpython.lastIndex[path] + 1;\n",
       "            }\n",
       "\n",
       "            catboostIpython.lastIndex[path] = items.length - 1;\n",
       "\n",
       "            for (var i = firstIndex; i < items.length; i++) {\n",
       "                chunks.push(items[i]);\n",
       "            }\n",
       "\n",
       "            catboostIpython.addMeta(data.path, content.data.meta);\n",
       "\n",
       "            catboostIpython.addPoints(parent, {\n",
       "                chunks: chunks,\n",
       "                train: data.name,\n",
       "                path: data.path\n",
       "            });\n",
       "        },\n",
       "\n",
       "        render_charts: function () {\n",
       "            this.process_all(this.el, {\n",
       "                data: this.model.get('data')\n",
       "            });\n",
       "\n",
       "            return this;\n",
       "        }\n",
       "    });\n",
       "\n",
       "    return {\n",
       "        CatboostIpythonWidgetView: CatboostIpythonWidget\n",
       "    };\n",
       "});\n",
       "\n",
       "            </script>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0035d1240f7945f89e9d99d1ca977261",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     iterations  test-MultiClass-mean  test-MultiClass-std  \\\n",
      "0             0              0.677018             0.000533   \n",
      "1             1              0.661722             0.000499   \n",
      "2             2              0.648263             0.001406   \n",
      "3             3              0.634810             0.001353   \n",
      "4             4              0.624112             0.003060   \n",
      "..          ...                   ...                  ...   \n",
      "495         495              0.012217             0.001967   \n",
      "496         496              0.012199             0.001939   \n",
      "497         497              0.012168             0.001936   \n",
      "498         498              0.012172             0.001932   \n",
      "499         499              0.012140             0.001966   \n",
      "\n",
      "     train-MultiClass-mean  train-MultiClass-std  \\\n",
      "0                 0.676918              0.000395   \n",
      "1                 0.661407              0.000401   \n",
      "2                 0.647169              0.000700   \n",
      "3                 0.633744              0.000231   \n",
      "4                 0.620965              0.000430   \n",
      "..                     ...                   ...   \n",
      "495               0.129002              0.003087   \n",
      "496               0.128970              0.003085   \n",
      "497               0.128940              0.003079   \n",
      "498               0.128917              0.003076   \n",
      "499               0.128874              0.003118   \n",
      "\n",
      "     test-Accuracy:use_weights=true-mean  test-Accuracy:use_weights=true-std  \\\n",
      "0                               0.849094                            0.000673   \n",
      "1                               0.849700                            0.000866   \n",
      "2                               0.848844                            0.000830   \n",
      "3                               0.850688                            0.000392   \n",
      "4                               0.850853                            0.001104   \n",
      "..                                   ...                                 ...   \n",
      "495                             0.996421                            0.000944   \n",
      "496                             0.996421                            0.000944   \n",
      "497                             0.996404                            0.000971   \n",
      "498                             0.996387                            0.001001   \n",
      "499                             0.996387                            0.001001   \n",
      "\n",
      "     train-Accuracy:use_weights=true-mean  \\\n",
      "0                                0.849495   \n",
      "1                                0.849874   \n",
      "2                                0.850138   \n",
      "3                                0.850845   \n",
      "4                                0.851271   \n",
      "..                                    ...   \n",
      "495                              0.943488   \n",
      "496                              0.943512   \n",
      "497                              0.943504   \n",
      "498                              0.943529   \n",
      "499                              0.943580   \n",
      "\n",
      "     train-Accuracy:use_weights=true-std  \\\n",
      "0                               0.000367   \n",
      "1                               0.000376   \n",
      "2                               0.000637   \n",
      "3                               0.000536   \n",
      "4                               0.001174   \n",
      "..                                   ...   \n",
      "495                             0.001553   \n",
      "496                             0.001604   \n",
      "497                             0.001605   \n",
      "498                             0.001564   \n",
      "499                             0.001590   \n",
      "\n",
      "     test-Accuracy:use_weights=false-mean  \\\n",
      "0                                0.849486   \n",
      "1                                0.850225   \n",
      "2                                0.849385   \n",
      "3                                0.851015   \n",
      "4                                0.851250   \n",
      "..                                    ...   \n",
      "495                              0.996438   \n",
      "496                              0.996438   \n",
      "497                              0.996421   \n",
      "498                              0.996404   \n",
      "499                              0.996404   \n",
      "\n",
      "     test-Accuracy:use_weights=false-std  \\\n",
      "0                               0.000581   \n",
      "1                               0.001000   \n",
      "2                               0.000975   \n",
      "3                               0.000354   \n",
      "4                               0.000791   \n",
      "..                                   ...   \n",
      "495                             0.000953   \n",
      "496                             0.000953   \n",
      "497                             0.000981   \n",
      "498                             0.001010   \n",
      "499                             0.001010   \n",
      "\n",
      "     train-Accuracy:use_weights=false-mean  \\\n",
      "0                                 0.849864   \n",
      "1                                 0.850376   \n",
      "2                                 0.850687   \n",
      "3                                 0.851166   \n",
      "4                                 0.851687   \n",
      "..                                     ...   \n",
      "495                               0.943687   \n",
      "496                               0.943713   \n",
      "497                               0.943704   \n",
      "498                               0.943729   \n",
      "499                               0.943780   \n",
      "\n",
      "     train-Accuracy:use_weights=false-std  \n",
      "0                                0.000469  \n",
      "1                                0.000319  \n",
      "2                                0.000811  \n",
      "3                                0.000912  \n",
      "4                                0.001072  \n",
      "..                                    ...  \n",
      "495                              0.001538  \n",
      "496                              0.001588  \n",
      "497                              0.001591  \n",
      "498                              0.001550  \n",
      "499                              0.001575  \n",
      "\n",
      "[500 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "separation = {\n",
    "              'responsive': 0,\n",
    "              'unresponsive':1\n",
    "             }\n",
    "\n",
    "results = train_test_model(n_class=2, labels=separation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1370,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_2 = results\n",
    "results_2_train_accuracy_max = 0.943580\n",
    "results_2_test_accuracy_max = 0.996387"
   ]
  }
 ],
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